diff --git a/README.md b/README.md
index 4cadded..fb7e533 100644
--- a/README.md
+++ b/README.md
@@ -1,151 +1,32 @@
-# Python interface to Stanford Core NLP tools v1.3.3
+# A Python wrapper for the Java Stanford Core NLP tools
-This is a Python wrapper for Stanford University's NLP group's Java-based [CoreNLP tools](http://nlp.stanford.edu/software/corenlp.shtml). It can either be imported as a module or run as a JSON-RPC server. Because it uses many large trained models (requiring 3GB RAM on 64-bit machines and usually a few minutes loading time), most applications will probably want to run it as a server.
+This is a fork of [stanford-corenlp-python](https://github.com/dasmith/stanford-corenlp-python), originally written by Dustin Smith and it incorporates the fixes made by Hioroyoshi Komatsu [corenlp-python] (https://bitbucket.org/torotoki/corenlp-python).
+## New changes:
- * Python interface to Stanford CoreNLP tools: tagging, phrase-structure parsing, dependency parsing, named entity resolution, and coreference resolution.
- * Runs an JSON-RPC server that wraps the Java server and outputs JSON.
- * Outputs parse trees which can be used by [nltk](http://nltk.googlecode.com/svn/trunk/doc/howto/tree.html).
+The original wrapper only worked for extreemly short texts and it did not accept files. This new version cleans the raw text files that are placed in the "raw_text" folder, places the cleaned files into the "clean_text" folder, runs the Stanford tools on those cleaned files and creates a large xml file which is placed in the xml folder. The "parse_xml_output()" calls the Stanford tools and parses the xml file to create a python object that is identical to the object that was created in the old version, except that the text length is now unlimited. All changes were mad to one file: "corenlp.py", which can by found in the "corenlp" folder. For more information, please consult the "README" file (by Hioroyoshi Komatsu) in the "corenlp-python" folder! I am constantly working on this project as these tools help me in my work and I welcome any feedback [jac2130@columbia.edu].
+## New Requirements
+ * [xmltodict](https://github.com/martinblech/xmltodict)
-It requires [pexpect](http://www.noah.org/wiki/pexpect) and (optionally) [unidecode](http://pypi.python.org/pypi/Unidecode) to handle non-ASCII text. This script includes and uses code from [jsonrpc](http://www.simple-is-better.org/rpc/) and [python-progressbar](http://code.google.com/p/python-progressbar/).
+## To use the new feature:
-It runs the Stanford CoreNLP jar in a separate process, communicates with the java process using its command-line interface, and makes assumptions about the output of the parser in order to parse it into a Python dict object and transfer it using JSON. The parser will break if the output changes significantly, but it has been tested on **Core NLP tools version 1.3.3** released 2012-07-09.
+ import sys
-## Download and Usage
+ sys.path.append("[full-path]/corenlp-wrapper/corenlp")
-To use this program you must [download](http://nlp.stanford.edu/software/corenlp.shtml#Download) and unpack the tgz file containing Stanford's CoreNLP package. By default, `corenlp.py` looks for the Stanford Core NLP folder as a subdirectory of where the script is being run.
+ from collections import OrderedDict
-In other words:
+ from corenlp import StanfordCoreNLP
- sudo pip install pexpect unidecode # unidecode is optional
- git clone git://github.com/dasmith/stanford-corenlp-python.git
- cd stanford-corenlp-python
- wget http://nlp.stanford.edu/software/stanford-corenlp-2012-07-09.tgz
- tar xvfz stanford-corenlp-2012-07-09.tgz
+ corenlp_dir = "[full-path]/corenlp-wrapper/stanford-corenlp-full-2013-04-04"
-Then, to launch a server:
+ corenlp = StanfordCoreNLP(corenlp_dir)
- python corenlp.py
+ parse_dict=eval(corenlp.parse())
-Optionally, you can specify a host or port:
+If you instead type:
- python corenlp.py -H 0.0.0.0 -p 3456
-
-That will run a public JSON-RPC server on port 3456.
-
-Assuming you are running on port 8080, the code in `client.py` shows an example parse:
-
- import jsonrpc
- from simplejson import loads
- server = jsonrpc.ServerProxy(jsonrpc.JsonRpc20(),
- jsonrpc.TransportTcpIp(addr=("127.0.0.1", 8080)))
-
- result = loads(server.parse("Hello world. It is so beautiful"))
- print "Result", result
-
-That returns a dictionary containing the keys `sentences` and (when applicable) `corefs`. The key `sentences` contains a list of dictionaries for each sentence, which contain `parsetree`, `text`, `tuples` containing the dependencies, and `words`, containing information about parts of speech, NER, etc:
-
- {u'sentences': [{u'parsetree': u'(ROOT (S (VP (NP (INTJ (UH Hello)) (NP (NN world)))) (. !)))',
- u'text': u'Hello world!',
- u'tuples': [[u'dep', u'world', u'Hello'],
- [u'root', u'ROOT', u'world']],
- u'words': [[u'Hello',
- {u'CharacterOffsetBegin': u'0',
- u'CharacterOffsetEnd': u'5',
- u'Lemma': u'hello',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'UH'}],
- [u'world',
- {u'CharacterOffsetBegin': u'6',
- u'CharacterOffsetEnd': u'11',
- u'Lemma': u'world',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'NN'}],
- [u'!',
- {u'CharacterOffsetBegin': u'11',
- u'CharacterOffsetEnd': u'12',
- u'Lemma': u'!',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'.'}]]},
- {u'parsetree': u'(ROOT (S (NP (PRP It)) (VP (VBZ is) (ADJP (RB so) (JJ beautiful))) (. .)))',
- u'text': u'It is so beautiful.',
- u'tuples': [[u'nsubj', u'beautiful', u'It'],
- [u'cop', u'beautiful', u'is'],
- [u'advmod', u'beautiful', u'so'],
- [u'root', u'ROOT', u'beautiful']],
- u'words': [[u'It',
- {u'CharacterOffsetBegin': u'14',
- u'CharacterOffsetEnd': u'16',
- u'Lemma': u'it',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'PRP'}],
- [u'is',
- {u'CharacterOffsetBegin': u'17',
- u'CharacterOffsetEnd': u'19',
- u'Lemma': u'be',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'VBZ'}],
- [u'so',
- {u'CharacterOffsetBegin': u'20',
- u'CharacterOffsetEnd': u'22',
- u'Lemma': u'so',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'RB'}],
- [u'beautiful',
- {u'CharacterOffsetBegin': u'23',
- u'CharacterOffsetEnd': u'32',
- u'Lemma': u'beautiful',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'JJ'}],
- [u'.',
- {u'CharacterOffsetBegin': u'32',
- u'CharacterOffsetEnd': u'33',
- u'Lemma': u'.',
- u'NamedEntityTag': u'O',
- u'PartOfSpeech': u'.'}]]}],
- u'coref': [[[[u'It', 1, 0, 0, 1], [u'Hello world', 0, 1, 0, 2]]]]}
-
-To use it in a regular script or to edit/debug it (because errors via RPC are opaque), load the module instead:
-
- from corenlp import *
- corenlp = StanfordCoreNLP() # wait a few minutes...
- corenlp.parse("Parse it")
-
-
-
-
-## Questions
-
-**Stanford CoreNLP tools require a large amount of free memory**. Java 5+ uses about 50% more RAM on 64-bit machines than 32-bit machines. 32-bit machine users can lower the memory requirements by changing `-Xmx3g` to `-Xmx2g` or even less.
-If pexpect timesout while loading models, check to make sure you have enough memory and can run the server alone without your kernel killing the java process:
-
- java -cp stanford-corenlp-2012-07-09.jar:stanford-corenlp-2012-07-06-models.jar:xom.jar:joda-time.jar -Xmx3g edu.stanford.nlp.pipeline.StanfordCoreNLP -props default.properties
-
-You can reach me, Dustin Smith, by sending a message on GitHub or through email (contact information is available [on my webpage](http://web.media.mit.edu/~dustin)).
-
-
-# Contributors
-
-This is free and open source software and has benefited from the contribution and feedback of others. Like Stanford's CoreNLP tools, it is covered under the [GNU General Public License v2 +](http://www.gnu.org/licenses/gpl-2.0.html), which in short means that modifications to this program must maintain the same free and open source distribution policy.
-
-This project has benefited from the contributions of:
-
- * @jcc Justin Cheng
- * Abhaya Agarwal
-
-## Related Projects
-
-These two projects are python wrappers for the [Stanford Parser](http://nlp.stanford.edu/software/lex-parser.shtml), which includes the Stanford Parser, although the Stanford Parser is another project.
- - [stanford-parser-python](http://projects.csail.mit.edu/spatial/Stanford_Parser) uses [JPype](http://jpype.sourceforge.net/) (interface to JVM)
- - [stanford-parser-jython](http://blog.gnucom.cc/2010/using-the-stanford-parser-with-jython/) uses Python
+ parse_dict=eval(corenlp.parse(text))
+where "text" is any non-empty string, the behavior is identical to the behavior before the new changes. Thus, if you want to use the new feature, you can just leave the text blank and place your raw files into the "raw_files" folder. The rest is as before.
diff --git a/LICENSE b/corenlp-wrapper/LICENSE
similarity index 100%
rename from LICENSE
rename to corenlp-wrapper/LICENSE
diff --git a/corenlp-wrapper/MANIFEST b/corenlp-wrapper/MANIFEST
new file mode 100644
index 0000000..3e64867
--- /dev/null
+++ b/corenlp-wrapper/MANIFEST
@@ -0,0 +1,7 @@
+# file GENERATED by distutils, do NOT edit
+setup.py
+corenlp/__init__.py
+corenlp/client.py
+corenlp/corenlp.py
+corenlp/default.properties
+corenlp/progressbar.py
diff --git a/corenlp-wrapper/README.md b/corenlp-wrapper/README.md
new file mode 100644
index 0000000..590a02c
--- /dev/null
+++ b/corenlp-wrapper/README.md
@@ -0,0 +1,289 @@
+# A Python wrapper of Stanford Core NLP tools
+
+
+This is a fork of [stanford-corenlp-python](https://github.com/dasmith/stanford-corenlp-python).
+
+## Edited
+ * Update to Stanford CoreNLP v1.3.5
+ * Fix many bugs & improve performance
+ * Using jsonrpclib for stability and performance
+ * Can edit the constants as argument such as Stanford Core NLP directory.
+ * Adjust parameters not to timeout in high load
+ * Packaging (beta)
+
+## Requirements
+ * [jsonrpclib](https://github.com/joshmarshall/jsonrpclib)
+ * [pexpect](http://www.noah.org/wiki/pexpect)
+ * [unidecode](http://pypi.python.org/pypi/Unidecode) (optionally)
+
+## Download and Usage
+
+To use this program you must [download](http://nlp.stanford.edu/software/corenlp.shtml#Download) and unpack the zip file containing Stanford's CoreNLP package. By default, `corenlp.py` looks for the Stanford Core NLP folder as a subdirectory of where the script is being run.
+
+
+In other words:
+
+ sudo pip install jsonrpclib pexpect unidecode # unidecode is optional
+ git clone https://bitbucket.org/torotoki/corenlp-python.git
+ cd corenlp-python
+ wget http://nlp.stanford.edu/software/stanford-corenlp-full-2013-04-04.zip
+ unzip stanford-corenlp-full-2013-04-04.zip
+
+Then, to launch a server:
+
+ python corenlp/corenlp.py
+
+Optionally, you can specify a host or port:
+
+ python corenlp/corenlp.py -H 0.0.0.0 -p 3456
+
+That will run a public JSON-RPC server on port 3456.
+And you can specify Stanford CoreNLP directory:
+
+ python corenlp/corenlp.py -S stanford-corenlp-full-2013-04-04/
+
+
+Assuming you are running on port 8080 and CoreNLP directory is `stanford-corenlp-full-2013-04-04/` in current directory, the code in `client.py` shows an example parse:
+
+ import jsonrpclib
+ from simplejson import loads
+ server = jsonrpclib.Server("http://localhost:8080")
+
+ result = loads(server.parse("Hello world. It is so beautiful"))
+ print "Result", result
+
+That returns a dictionary containing the keys `sentences` and (when applicable) `corefs`. The key `sentences` contains a list of dictionaries for each sentence, which contain `parsetree`, `text`, `tuples` containing the dependencies, and `words`, containing information about parts of speech, NER, etc:
+
+ {u'sentences': [{u'parsetree': u'(ROOT (S (VP (NP (INTJ (UH Hello)) (NP (NN world)))) (. !)))',
+ u'text': u'Hello world!',
+ u'tuples': [[u'dep', u'world', u'Hello'],
+ [u'root', u'ROOT', u'world']],
+ u'words': [[u'Hello',
+ {u'CharacterOffsetBegin': u'0',
+ u'CharacterOffsetEnd': u'5',
+ u'Lemma': u'hello',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'UH'}],
+ [u'world',
+ {u'CharacterOffsetBegin': u'6',
+ u'CharacterOffsetEnd': u'11',
+ u'Lemma': u'world',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'NN'}],
+ [u'!',
+ {u'CharacterOffsetBegin': u'11',
+ u'CharacterOffsetEnd': u'12',
+ u'Lemma': u'!',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'.'}]]},
+ {u'parsetree': u'(ROOT (S (NP (PRP It)) (VP (VBZ is) (ADJP (RB so) (JJ beautiful))) (. .)))',
+ u'text': u'It is so beautiful.',
+ u'tuples': [[u'nsubj', u'beautiful', u'It'],
+ [u'cop', u'beautiful', u'is'],
+ [u'advmod', u'beautiful', u'so'],
+ [u'root', u'ROOT', u'beautiful']],
+ u'words': [[u'It',
+ {u'CharacterOffsetBegin': u'14',
+ u'CharacterOffsetEnd': u'16',
+ u'Lemma': u'it',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'PRP'}],
+ [u'is',
+ {u'CharacterOffsetBegin': u'17',
+ u'CharacterOffsetEnd': u'19',
+ u'Lemma': u'be',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'VBZ'}],
+ [u'so',
+ {u'CharacterOffsetBegin': u'20',
+ u'CharacterOffsetEnd': u'22',
+ u'Lemma': u'so',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'RB'}],
+ [u'beautiful',
+ {u'CharacterOffsetBegin': u'23',
+ u'CharacterOffsetEnd': u'32',
+ u'Lemma': u'beautiful',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'JJ'}],
+ [u'.',
+ {u'CharacterOffsetBegin': u'32',
+ u'CharacterOffsetEnd': u'33',
+ u'Lemma': u'.',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'.'}]]}],
+ u'coref': [[[[u'It', 1, 0, 0, 1], [u'Hello world', 0, 1, 0, 2]]]]}
+
+To use it in a regular script or to edit/debug it (because errors via RPC are opaque), load the module instead:
+
+ from corenlp import StanfordCoreNLP
+ corenlp_dir = "stanford-corenlp-full-2013-04-04/"
+ corenlp = StanfordCoreNLP(corenlp_dir) # wait a few minutes...
+ corenlp.parse("Parse it")
+
+
+
+
+Following original README in stanford-corenlp-python.
+
+-------------------------------------
+
+ Python interface to Stanford Core NLP tools v1.3.3
+
+This is a Python wrapper for Stanford University's NLP group's Java-based [CoreNLP tools](http://nlp.stanford.edu/software/corenlp.shtml). It can either be imported as a module or run as a JSON-RPC server. Because it uses many large trained models (requiring 3GB RAM on 64-bit machines and usually a few minutes loading time), most applications will probably want to run it as a server.
+
+
+ * Python interface to Stanford CoreNLP tools: tagging, phrase-structure parsing, dependency parsing, named entity resolution, and coreference resolution.
+ * Runs an JSON-RPC server that wraps the Java server and outputs JSON.
+ * Outputs parse trees which can be used by [nltk](http://nltk.googlecode.com/svn/trunk/doc/howto/tree.html).
+
+
+It requires [pexpect](http://www.noah.org/wiki/pexpect) and (optionally) [unidecode](http://pypi.python.org/pypi/Unidecode) to handle non-ASCII text. This script includes and uses code from [jsonrpc](http://www.simple-is-better.org/rpc/) and [python-progressbar](http://code.google.com/p/python-progressbar/).
+
+It runs the Stanford CoreNLP jar in a separate process, communicates with the java process using its command-line interface, and makes assumptions about the output of the parser in order to parse it into a Python dict object and transfer it using JSON. The parser will break if the output changes significantly, but it has been tested on **Core NLP tools version 1.3.3** released 2012-07-09.
+
+## Download and Usage
+
+To use this program you must [download](http://nlp.stanford.edu/software/corenlp.shtml#Download) and unpack the tgz file containing Stanford's CoreNLP package. By default, `corenlp.py` looks for the Stanford Core NLP folder as a subdirectory of where the script is being run.
+
+In other words:
+
+ sudo pip install pexpect unidecode # unidecode is optional
+ git clone git://github.com/dasmith/stanford-corenlp-python.git
+ cd stanford-corenlp-python
+ wget http://nlp.stanford.edu/software/stanford-corenlp-2012-07-09.tgz
+ tar xvfz stanford-corenlp-2012-07-09.tgz
+
+Then, to launch a server:
+
+ python corenlp.py
+
+Optionally, you can specify a host or port:
+
+ python corenlp.py -H 0.0.0.0 -p 3456
+
+That will run a public JSON-RPC server on port 3456.
+
+Assuming you are running on port 8080, the code in `client.py` shows an example parse:
+
+ import jsonrpc
+ from simplejson import loads
+ server = jsonrpc.ServerProxy(jsonrpc.JsonRpc20(),
+ jsonrpc.TransportTcpIp(addr=("127.0.0.1", 8080)))
+
+ result = loads(server.parse("Hello world. It is so beautiful"))
+ print "Result", result
+
+That returns a dictionary containing the keys `sentences` and (when applicable) `corefs`. The key `sentences` contains a list of dictionaries for each sentence, which contain `parsetree`, `text`, `tuples` containing the dependencies, and `words`, containing information about parts of speech, NER, etc:
+
+ {u'sentences': [{u'parsetree': u'(ROOT (S (VP (NP (INTJ (UH Hello)) (NP (NN world)))) (. !)))',
+ u'text': u'Hello world!',
+ u'tuples': [[u'dep', u'world', u'Hello'],
+ [u'root', u'ROOT', u'world']],
+ u'words': [[u'Hello',
+ {u'CharacterOffsetBegin': u'0',
+ u'CharacterOffsetEnd': u'5',
+ u'Lemma': u'hello',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'UH'}],
+ [u'world',
+ {u'CharacterOffsetBegin': u'6',
+ u'CharacterOffsetEnd': u'11',
+ u'Lemma': u'world',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'NN'}],
+ [u'!',
+ {u'CharacterOffsetBegin': u'11',
+ u'CharacterOffsetEnd': u'12',
+ u'Lemma': u'!',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'.'}]]},
+ {u'parsetree': u'(ROOT (S (NP (PRP It)) (VP (VBZ is) (ADJP (RB so) (JJ beautiful))) (. .)))',
+ u'text': u'It is so beautiful.',
+ u'tuples': [[u'nsubj', u'beautiful', u'It'],
+ [u'cop', u'beautiful', u'is'],
+ [u'advmod', u'beautiful', u'so'],
+ [u'root', u'ROOT', u'beautiful']],
+ u'words': [[u'It',
+ {u'CharacterOffsetBegin': u'14',
+ u'CharacterOffsetEnd': u'16',
+ u'Lemma': u'it',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'PRP'}],
+ [u'is',
+ {u'CharacterOffsetBegin': u'17',
+ u'CharacterOffsetEnd': u'19',
+ u'Lemma': u'be',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'VBZ'}],
+ [u'so',
+ {u'CharacterOffsetBegin': u'20',
+ u'CharacterOffsetEnd': u'22',
+ u'Lemma': u'so',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'RB'}],
+ [u'beautiful',
+ {u'CharacterOffsetBegin': u'23',
+ u'CharacterOffsetEnd': u'32',
+ u'Lemma': u'beautiful',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'JJ'}],
+ [u'.',
+ {u'CharacterOffsetBegin': u'32',
+ u'CharacterOffsetEnd': u'33',
+ u'Lemma': u'.',
+ u'NamedEntityTag': u'O',
+ u'PartOfSpeech': u'.'}]]}],
+ u'coref': [[[[u'It', 1, 0, 0, 1], [u'Hello world', 0, 1, 0, 2]]]]}
+
+To use it in a regular script or to edit/debug it (because errors via RPC are opaque), load the module instead:
+
+ from corenlp import *
+ corenlp = StanfordCoreNLP() # wait a few minutes...
+ corenlp.parse("Parse it")
+
+
+
+
+## Questions
+
+**Stanford CoreNLP tools require a large amount of free memory**. Java 5+ uses about 50% more RAM on 64-bit machines than 32-bit machines. 32-bit machine users can lower the memory requirements by changing `-Xmx3g` to `-Xmx2g` or even less.
+If pexpect timesout while loading models, check to make sure you have enough memory and can run the server alone without your kernel killing the java process:
+
+ java -cp stanford-corenlp-2012-07-09.jar:stanford-corenlp-2012-07-06-models.jar:xom.jar:joda-time.jar -Xmx3g edu.stanford.nlp.pipeline.StanfordCoreNLP -props default.properties
+
+You can reach me, Dustin Smith, by sending a message on GitHub or through email (contact information is available [on my webpage](http://web.media.mit.edu/~dustin)).
+
+
+# Contributors
+
+This is free and open source software and has benefited from the contribution and feedback of others. Like Stanford's CoreNLP tools, it is covered under the [GNU General Public License v2 +](http://www.gnu.org/licenses/gpl-2.0.html), which in short means that modifications to this program must maintain the same free and open source distribution policy.
+
+This project has benefited from the contributions of:
+
+ * @jcc Justin Cheng
+ * Abhaya Agarwal
+
+## Related Projects
+
+These two projects are python wrappers for the [Stanford Parser](http://nlp.stanford.edu/software/lex-parser.shtml), which includes the Stanford Parser, although the Stanford Parser is another project.
+ - [stanford-parser-python](http://projects.csail.mit.edu/spatial/Stanford_Parser) uses [JPype](http://jpype.sourceforge.net/) (interface to JVM)
+ - [stanford-parser-jython](http://blog.gnucom.cc/2010/using-the-stanford-parser-with-jython/) uses Python
diff --git a/corenlp-wrapper/corenlp/__init__.py b/corenlp-wrapper/corenlp/__init__.py
new file mode 100644
index 0000000..2ed1ffc
--- /dev/null
+++ b/corenlp-wrapper/corenlp/__init__.py
@@ -0,0 +1,12 @@
+# corenlp
+# Copyright 2013- Hiroyoshi Komatsu
+# See LICENSE for details.
+
+"""
+Stanford CoreNLP Python wrapper
+"""
+__version__ = '1.0.3'
+__author__ = 'Hiroyoshi Komatsu'
+__license__ = 'GNU v2+'
+
+from corenlp import StanfordCoreNLP, ParserError, TimeoutError, ProcessError
diff --git a/client.py b/corenlp-wrapper/corenlp/client.py
old mode 100644
new mode 100755
similarity index 60%
rename from client.py
rename to corenlp-wrapper/corenlp/client.py
index 11097ce..c5c4a98
--- a/client.py
+++ b/corenlp-wrapper/corenlp/client.py
@@ -1,12 +1,13 @@
import json
-from jsonrpc import ServerProxy, JsonRpc20, TransportTcpIp
+# from jsonrpc import ServerProxy, JsonRpc20, TransportTcpIp
+import jsonrpclib
from pprint import pprint
+
class StanfordNLP:
- def __init__(self):
- self.server = ServerProxy(JsonRpc20(),
- TransportTcpIp(addr=("127.0.0.1", 8080)))
-
+ def __init__(self, port_number=8080):
+ self.server = jsonrpclib.Server("http://localhost:%d" % port_number)
+
def parse(self, text):
return json.loads(self.server.parse(text))
diff --git a/corenlp-wrapper/corenlp/corenlp.py b/corenlp-wrapper/corenlp/corenlp.py
new file mode 100755
index 0000000..c3413ab
--- /dev/null
+++ b/corenlp-wrapper/corenlp/corenlp.py
@@ -0,0 +1,410 @@
+#!/usr/bin/env python
+#
+# corenlp - Python interface to Stanford Core NLP tools
+# Copyright (c) 2012 Dustin Smith
+# https://github.com/dasmith/stanford-corenlp-python
+#
+# This program is free software; you can redistribute it and/or
+# modify it under the terms of the GNU General Public License
+# as published by the Free Software Foundation; either version 2
+# of the License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
+
+
+import json, optparse, os, re, sys, time, traceback
+import pexpect
+from progressbar import ProgressBar, Fraction
+from unidecode import unidecode
+from jsonrpclib.SimpleJSONRPCServer import SimpleJSONRPCServer
+import nltk, nltk.data
+
+
+class bc:
+ HEADER = '\033[95m'
+ OKBLUE = '\033[94m'
+ OKGREEN = '\033[92m'
+ WARNING = '\033[93m'
+ FAIL = '\033[91m'
+ ENDC = '\033[0m'
+
+
+VERBOSE = True
+STATE_START, STATE_TEXT, STATE_WORDS, STATE_TREE, STATE_DEPENDENCY, STATE_COREFERENCE = 0, 1, 2, 3, 4, 5
+WORD_PATTERN = re.compile('\[([^\]]+)\]')
+CR_PATTERN = re.compile(r"\((\d*),(\d)*,\[(\d*),(\d*)\)\) -> \((\d*),(\d)*,\[(\d*),(\d*)\)\), that is: \"(.*)\" -> \"(.*)\"")
+
+class ProcessError(Exception):
+ def __init__(self, value):
+ self.value = value
+ def __str__(self):
+ return repr(self.value)
+
+class ParserError(Exception):
+ def __init__(self, value):
+ self.value = value
+ def __str__(self):
+ return repr(self.value)
+
+class TimeoutError(Exception):
+ def __init__(self, value):
+ self.value = value
+ def __str__(self):
+ return repr(self.value)
+
+def clean_raw_text(files=[]):
+ #cleans all files contained in the directory "files/raw_text/" and places
+ #them into the "files/clean_text" directory.
+ import re
+ import nltk, nltk.data
+
+ sent_detector=nltk.data.load('tokenizers/punkt/english.pickle')
+
+ raw_files=['files/raw_text/' + f for f in os.listdir('files/raw_text/')] if not files else files
+ clean_files=['files/clean_text/' + raw.split('/')[-1][:-4] + '_clean.txt' for raw in raw_files]
+
+ for raw, clean in zip(raw_files, clean_files):
+ raw_text=open(raw, 'r').read()
+ text=re.sub(r'-+(\n)\s*', '', raw_text)
+ text=re.sub(r'(\n)+', '', text)
+ text= ' '.join([' '.join(nltk.word_tokenize(sent)) for sent in sent_detector.tokenize(text.strip())])
+ open(clean, 'w').write(text)
+
+
+def remove_id(word):
+ """Removes the numeric suffix from the parsed recognized words: e.g. 'word-2' > 'word' """
+ return word.count("-") == 0 and word or word[0:word.rindex("-")]
+
+
+def parse_bracketed(s):
+ '''Parse word features [abc=... def = ...]
+ Also manages to parse out features that have XML within them
+ '''
+ word = None
+ attrs = {}
+ temp = {}
+ # Substitute XML tags, to replace them later
+ for i, tag in enumerate(re.findall(r"(<[^<>]+>.*<\/[^<>]+>)", s)):
+ temp["^^^%d^^^" % i] = tag
+ s = s.replace(tag, "^^^%d^^^" % i)
+ # Load key-value pairs, substituting as necessary
+ for attr, val in re.findall(r"([^=\s]*)=([^=\s]*)", s):
+ if val in temp:
+ val = temp[val]
+ if attr == 'Text':
+ word = val
+ else:
+ attrs[attr] = val
+ return (word, attrs)
+
+def parse_xml_output():
+ import os
+ import nltk, nltk.data
+ import xmltodict
+ from collections import OrderedDict
+ """Because interaction with the command-line interface of the CoreNLP
+ tools is limited to very short text bits, it is necessary to parse xml
+ output"""
+ #First, we change to the directory where we place the xml files from the
+ #parser:
+ here = os.path.dirname(os.path.abspath( __file__ ))
+ os.chdir(here)
+ os.chdir('../../files/xml')
+
+ #we get a list of the cleaned files that we want to parse:
+
+ files=['../clean_text/' + f for f in os.listdir('../clean_text')]
+
+ #creating the file list of files to parse; the stanford tools require a file of file-names, with each file name on its own line:
+
+ with open('../files.txt', 'w') as write_files:
+ write_files.write('\n'.join(files))
+
+ sent_detector=nltk.data.load('tokenizers/punkt/english.pickle')
+ lines=[]
+ #extracting the sentences from the text:
+
+ [lines.extend(sent_detector.tokenize(open(text, 'r').read().strip())) for text in files]
+
+ command='java -Xmx3g -cp ../../corenlp-wrapper/stanford-corenlp-full-2013-04-04/stanford-corenlp-1.3.5.jar:../../corenlp-wrapper/stanford-corenlp-full-2013-04-04/stanford-corenlp-1.3.5-models.jar:../../corenlp-wrapper/stanford-corenlp-full-2013-04-04/xom.jar:../../corenlp-wrapper/stanford-corenlp-full-2013-04-04/joda-time.jar:../../corenlp-wrapper/stanford-corenlp-full-2013-04-04/jollyday.jar edu.stanford.nlp.pipeline.StanfordCoreNLP -props ../../corenlp-wrapper/corenlp/default.properties -filelist ../files.txt'
+
+ #creates the xml file of parser output:
+
+ os.system(command)
+
+ #reading in the raw xml file:
+ xml=open(os.listdir('.')[0], 'r').read()
+
+ #turning the raw xml into a raw python dictionary:
+ raw_dict=xmltodict.parse(xml)
+
+ #making a raw sentence list of dictionaries:
+ raw_sent_list=raw_dict[u'root'][u'document'][u'sentences'][u'sentence']
+ #making a raw coref dictionary:
+ raw_coref_list=raw_dict[u'root'][u'document'][u'coreference'][u'coreference']
+
+ #cleaning up the list ...the problem is that this doesn't come in pairs, as the command line version:
+
+ coref_list=[[[eval(raw_coref_list[j][u'mention'][i]['sentence'])-1, eval(raw_coref_list[j][u'mention'][i]['head'])-1, eval(raw_coref_list[j][u'mention'][i]['start'])-1, eval(raw_coref_list[j][u'mention'][i]['end'])-1] for i in range(len(raw_coref_list[j][u'mention']))] for j in range(len(raw_coref_list))]
+
+ [[coref.insert(0,' '.join(lines[coref[0]].split()[coref[-2]:coref[-1]])) for coref in coref_list[j]] for j in range(len(coref_list))]
+ os.chdir('../..')
+
+ coref_list=[[[coref_list[j][i], coref_list[j][0]] for i in range(len(coref_list[j]))] for j in range(len(coref_list))]
+
+ sentences=[{'dependencies': [[dep['dep'][i]['@type'], dep['dep'][i]['governor']['#text'], dep['dep'][i]['dependent']['#text']] for dep in raw_sent_list[j][u'dependencies'] for i in range(len(dep['dep'])) if dep['@type']=='basic-dependencies'], 'text': lines[j], 'parsetree': str(raw_sent_list[j]['parse']), 'words': [[str(token['word']), OrderedDict([('NamedEntityTag', str(token['NER'])), ('CharacterOffsetEnd', str(token['CharacterOffsetEnd'])), ('CharacterOffsetBegin', str(token['CharacterOffsetBegin'])), ('PartOfSpeech', str(token['POS'])), ('Lemma', str(token['lemma']))])] for token in raw_sent_list[j]['tokens'][u'token']]} for j in range(len(lines))]
+
+ results={'coref':coref_list, 'sentences':sentences}
+
+ return results
+
+def parse_parser_results(text):
+ """ This is the nasty bit of code to interact with the command-line
+ interface of the CoreNLP tools. Takes a string of the parser results
+ and then returns a Python list of dictionaries, one for each parsed
+ sentence.
+ """
+ results = {"sentences": []}
+ state = STATE_START
+ for line in unidecode(text.decode('utf-8')).split("\n"):
+ line = line.strip()
+
+ if line.startswith("Sentence #"):
+ sentence = {'words':[], 'parsetree':[], 'dependencies':[]}
+ results["sentences"].append(sentence)
+ state = STATE_TEXT
+
+ elif state == STATE_TEXT:
+ sentence['text'] = line
+ state = STATE_WORDS
+
+ elif state == STATE_WORDS:
+ if not line.startswith("[Text="):
+ raise ParserError('Parse error. Could not find "[Text=" in: %s' % line)
+ for s in WORD_PATTERN.findall(line):
+ sentence['words'].append(parse_bracketed(s))
+ state = STATE_TREE
+
+ elif state == STATE_TREE:
+ if len(line) == 0:
+ state = STATE_DEPENDENCY
+ sentence['parsetree'] = " ".join(sentence['parsetree'])
+ else:
+ sentence['parsetree'].append(line)
+
+ elif state == STATE_DEPENDENCY:
+ if len(line) == 0:
+ state = STATE_COREFERENCE
+ else:
+ split_entry = re.split("\(|, ", line[:-1])
+ if len(split_entry) == 3:
+ rel, left, right = map(lambda x: remove_id(x), split_entry)
+ sentence['dependencies'].append(tuple([rel,left,right]))
+
+ elif state == STATE_COREFERENCE:
+ if "Coreference set" in line:
+ if 'coref' not in results:
+ results['coref'] = []
+ coref_set = []
+ results['coref'].append(coref_set)
+ else:
+ for src_i, src_pos, src_l, src_r, sink_i, sink_pos, sink_l, sink_r, src_word, sink_word in CR_PATTERN.findall(line):
+ src_i, src_pos, src_l, src_r = int(src_i)-1, int(src_pos)-1, int(src_l)-1, int(src_r)-1
+ sink_i, sink_pos, sink_l, sink_r = int(sink_i)-1, int(sink_pos)-1, int(sink_l)-1, int(sink_r)-1
+ coref_set.append(((src_word, src_i, src_pos, src_l, src_r), (sink_word, sink_i, sink_pos, sink_l, sink_r)))
+
+ return results
+
+
+class StanfordCoreNLP(object):
+ """
+ Command-line interaction with Stanford's CoreNLP java utilities.
+ Can be run as a JSON-RPC server or imported as a module.
+ """
+ def __init__(self, corenlp_path="stanford-corenlp-full-2013-04-04/", memory="3g"):
+ """
+ Checks the location of the jar files.
+ Spawns the server as a process.
+ """
+
+ jars = ["stanford-corenlp-1.3.5.jar",
+ "stanford-corenlp-1.3.5-models.jar",
+ "xom.jar",
+ "joda-time.jar",
+ "jollyday.jar"]
+
+ java_path = "java"
+ classname = "edu.stanford.nlp.pipeline.StanfordCoreNLP"
+ # include the properties file, so you can change defaults
+ # but any changes in output format will break parse_parser_results()
+ property_name = "default.properties"
+ current_dir_pr = os.path.dirname(os.path.abspath( __file__ )) +"/"+ property_name
+ if os.path.exists(property_name):
+ props = "-props %s" % (property_name)
+ elif os.path.exists(current_dir_pr):
+ props = "-props %s" % (current_dir_pr)
+ else:
+ raise Exception("Error! Cannot locate: default.properties")
+
+ # add and check classpaths
+ jars = [corenlp_path +"/"+ jar for jar in jars]
+ for jar in jars:
+ if not os.path.exists(jar):
+ raise Exception("Error! Cannot locate: %s" % jar)
+
+ # add memory limit on JVM
+ if memory:
+ limit = "-Xmx%s" % memory
+ else:
+ limit = ""
+
+ # spawn the server
+ start_corenlp = "%s %s -cp %s %s %s" % (java_path, limit, ':'.join(jars), classname, props)
+ if VERBOSE: print "===========================================\n", start_corenlp
+ self.corenlp = pexpect.spawn(start_corenlp)
+
+ # show progress bar while loading the models
+ if VERBOSE:
+ widgets = ['Loading Models: ', Fraction()]
+ pbar = ProgressBar(widgets=widgets, maxval=5, force_update=True).start()
+ self.corenlp.expect("done.", timeout=20) # Load pos tagger model (~5sec)
+ if VERBOSE: pbar.update(1)
+ self.corenlp.expect("done.", timeout=200) # Load NER-all classifier (~33sec)
+ if VERBOSE: pbar.update(2)
+ self.corenlp.expect("done.", timeout=600) # Load NER-muc classifier (~60sec)
+ if VERBOSE: pbar.update(3)
+ self.corenlp.expect("done.", timeout=600) # Load CoNLL classifier (~50sec)
+ if VERBOSE: pbar.update(4)
+ self.corenlp.expect("done.", timeout=200) # Loading PCFG (~3sec)
+ if VERBOSE: pbar.update(5)
+ self.corenlp.expect("Entering interactive shell.")
+ if VERBOSE: pbar.finish()
+
+ # interactive shell
+ self.corenlp.expect("\nNLP> ", timeout=3)
+
+ def close(self, force=True):
+ self.corenlp.terminate(force)
+
+ def isalive(self):
+ return self.corenlp.isalive()
+
+ def __del__(self):
+ # If our child process is still around, kill it
+ if self.isalive():
+ self.close()
+
+ def _parse(self, text):
+ """
+ This is the core interaction with the parser.
+
+ It returns a Python data-structure, while the parse()
+ function returns a JSON object
+ """
+
+ # CoreNLP interactive shell cannot recognize newline
+ if '\n' in text or '\r' in text:
+ to_send = re.sub("[\r\n]", " ", text).strip()
+ else:
+ to_send = text
+
+ # clean up anything leftover
+ def clean_up():
+ while True:
+ try:
+ self.corenlp.read_nonblocking (8192, 0.1)
+ except pexpect.TIMEOUT:
+ break
+ clean_up()
+ bytes_written = self.corenlp.sendline(to_send)
+ print bc.HEADER, "bytes written", bytes_written, bc.ENDC
+
+ # How much time should we give the parser to parse it?
+ # the idea here is that you increase the timeout as a
+ # function of the text's length.
+ # max_expected_time = max(5.0, 3 + len(to_send) / 5.0)
+ max_expected_time = max(300.0, len(to_send) / 3.0)*9000000000
+
+ # repeated_input = self.corenlp.except("\n") # confirm it
+ t = self.corenlp.expect(["\nNLP> ", pexpect.TIMEOUT, pexpect.EOF],
+ timeout=max_expected_time)
+ incoming = self.corenlp.before
+ if t == 1:
+ # TIMEOUT, clean up anything when raise pexpect.TIMEOUT error
+ clean_up()
+ print >>sys.stderr, {'error': "timed out after %f seconds" % max_expected_time,
+ 'input': to_send,
+ 'output': incoming}
+ raise TimeoutError("Timed out after %d seconds" % max_expected_time)
+ elif t == 2:
+ # EOF, probably crash CoreNLP process
+ print >>sys.stderr, {'error': "CoreNLP terminates abnormally while parsing",
+ 'input': to_send,
+ 'output': incoming}
+ self.corenlp.close()
+ raise ProcessError("CoreNLP process terminates abnormally while parsing")
+
+ if VERBOSE: print "%s\n%s" % ('='*40, incoming)
+ try:
+ results = parse_parser_results(incoming)
+ except Exception, e:
+ if VERBOSE: print traceback.format_exc()
+ raise e
+
+ return results
+
+ def raw_parse(self, text):
+ """
+ This function takes a text string, sends it to the Stanford parser,
+ reads in the result, parses the results and returns a list
+ with one dictionary entry for each parsed sentence.
+ """
+ return self._parse(text)
+
+ def parse(self, text='', files=[]):
+ """
+ This function takes a text string, sends it to the Stanford parser,
+ reads in the result, parses the results and returns a list
+ with one dictionary entry for each parsed sentence, in JSON format.
+ """
+ if text:
+ return json.dumps(self._parse(text))
+ else:
+ clean_raw_text(files=files)
+ return str(parse_xml_output())
+
+
+if __name__ == '__main__':
+ """
+ The code below starts an JSONRPC server
+ """
+ VERBOSE = True
+ parser = optparse.OptionParser(usage="%prog [OPTIONS]")
+ parser.add_option('-p', '--port', default='8080',
+ help='Port to serve on (default 8080)')
+ parser.add_option('-H', '--host', default='127.0.0.1',
+ help='Host to serve on (default localhost; 0.0.0.0 to make public)')
+ parser.add_option('-S', '--corenlp', default="stanford-corenlp-full-2013-04-04",
+ help='Stanford CoreNLP tool directory (default stanford-corenlp-full-2013-04-04/)')
+ options, args = parser.parse_args()
+ # server = jsonrpc.Server(jsonrpc.JsonRpc20(),
+ # jsonrpc.TransportTcpIp(addr=(options.host, int(options.port))))
+ try:
+ server = SimpleJSONRPCServer((options.host, int(options.port)))
+
+ nlp = StanfordCoreNLP(options.corenlp)
+ server.register_function(nlp.parse)
+
+ print 'Serving on http://%s:%s' % (options.host, options.port)
+ # server.serve()
+ server.serve_forever()
+ except KeyboardInterrupt:
+ print >>sys.stderr, "Bye."
+ exit()
diff --git a/default.properties b/corenlp-wrapper/corenlp/default.properties
similarity index 98%
rename from default.properties
rename to corenlp-wrapper/corenlp/default.properties
index e069ea9..ee99077 100644
--- a/default.properties
+++ b/corenlp-wrapper/corenlp/default.properties
@@ -57,7 +57,7 @@ annotators = tokenize, ssplit, pos, lemma, ner, parse, dcoref
#clean.xmltags = .*
# A set of tags which will force the end of a sentence. HTML example:
# you would not want to end on , but you would want to end on .
-# Once again, a regular expression.
+# Once again, a regular expression.
# (Blank means there are no sentence enders.)
#clean.sentenceendingtags =
# Whether or not to allow malformed xml
diff --git a/progressbar.py b/corenlp-wrapper/corenlp/progressbar.py
similarity index 100%
rename from progressbar.py
rename to corenlp-wrapper/corenlp/progressbar.py
diff --git a/corenlp-wrapper/setup.py b/corenlp-wrapper/setup.py
new file mode 100644
index 0000000..c5eeb8b
--- /dev/null
+++ b/corenlp-wrapper/setup.py
@@ -0,0 +1,43 @@
+import os
+from distutils.core import setup
+
+PACKAGE = "corenlp"
+NAME = "corenlp-python"
+DESCRIPTION = "A Stanford Core NLP wrapper"
+AUTHOR = "Hiroyoshi Komatsu"
+AUTHOR_EMAIL = "hiroyoshi.komat@gmail.com"
+URL = "https://bitbucket.org/torotoki/corenlp-python"
+VERSION = "1.0.3"
+
+# Utility function to read the README file.
+# Used for the long_description. It's nice, because now 1) we have a top level
+# README file and 2) it's easier to type in the README file than to put a raw
+# string in below ...
+def read(fname):
+ return open(os.path.join(os.path.dirname(__file__), fname)).read()
+
+setup(
+ name=NAME,
+ version=VERSION,
+ description=DESCRIPTION,
+ long_description=read("README.md"),
+ author=AUTHOR,
+ author_email=AUTHOR_EMAIL,
+ url=URL,
+ packages=['corenlp'],
+ package_dir = {'corenlp': 'corenlp'},
+ package_data = {
+ "corenlp": ["default.properties"]
+ },
+ # data_files = [
+ # ('corenlp', ["default.properties"]),
+ # ],
+ # package_data=find_package_data(
+ # PACKAGE,
+ # only_in_packages=False
+ # )
+ classifiers=[
+ "License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)",
+ "Programming Language :: Python",
+ ],
+)
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04.zip b/corenlp-wrapper/stanford-corenlp-full-2013-04-04.zip
new file mode 100644
index 0000000..26fae5a
Binary files /dev/null and b/corenlp-wrapper/stanford-corenlp-full-2013-04-04.zip differ
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/CoreNLP-to-HTML.xsl b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/CoreNLP-to-HTML.xsl
new file mode 100755
index 0000000..ccb9f8b
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/CoreNLP-to-HTML.xsl
@@ -0,0 +1,141 @@
+
+
+
+
+
+
+
+
+
+ Stanford CoreNLP XML Output
+
+ Document
+
+ | Sentences |
|---|
+
+ |
+
+
+
+ |
+
+
+ | Coreference resolution graph |
|---|
+ |
+
+ |
+
+
+
+
+
+
+
+
+ Sentence #
+
+
+ Tokens
+
+
+
+
+ Parse tree
+
+
+
+
+ Uncollapsed dependencies
+
+
+
+
+ Collapsed dependencies
+
+
+
+
+ Collapsed dependencies with CC processed
+
+
+
+
+
+
+
+ | Id |
+ Word |
+ Lemma |
+ Char begin |
+ Char end |
+ POS |
+ NER |
+ Normalized NER |
+
+
+
+ |
+ |
+ |
+ |
+ |
+ |
+ |
+ |
+
+
+
+
+
+
+
+
+
+
+
+
+ (
+ ^-
+ ,
+ ^-
+ )
+ (extra)
+
+
+
+
+
+
+ -
+
+
+ - sentence ,
+ headword
+ (gov)
+
+
+
+
+
+
+
+
+
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/LIBRARY-LICENSES b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/LIBRARY-LICENSES
new file mode 100644
index 0000000..e1fa147
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/LIBRARY-LICENSES
@@ -0,0 +1,20 @@
+xom-1.2.6.jar
+
+Url: http://www.xom.nu/
+
+License: http://www.xom.nu/license.xhtml
+
+LGPL
+Gnu lesser general public license
+
+-----------------------------------------
+
+joda-time-2.0.jar
+
+Url: http://joda-time.sourceforge.net/
+
+License: http://joda-time.sourceforge.net/license.html
+
+Apache License 2.0
+
+-----------------------------------------
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/LICENSE.txt b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/LICENSE.txt
new file mode 100644
index 0000000..60549be
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/LICENSE.txt
@@ -0,0 +1,340 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc.
+ 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The licenses for most software are designed to take away your
+freedom to share and change it. By contrast, the GNU General Public
+License is intended to guarantee your freedom to share and change free
+software--to make sure the software is free for all its users. This
+General Public License applies to most of the Free Software
+Foundation's software and to any other program whose authors commit to
+using it. (Some other Free Software Foundation software is covered by
+the GNU Library General Public License instead.) You can apply it to
+your programs, too.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+this service if you wish), that you receive source code or can get it
+if you want it, that you can change the software or use pieces of it
+in new free programs; and that you know you can do these things.
+
+ To protect your rights, we need to make restrictions that forbid
+anyone to deny you these rights or to ask you to surrender the rights.
+These restrictions translate to certain responsibilities for you if you
+distribute copies of the software, or if you modify it.
+
+ For example, if you distribute copies of such a program, whether
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+
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+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
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+
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+ (at your option) any later version.
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+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
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+
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+school, if any, to sign a "copyright disclaimer" for the program, if
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+
+ , 1 April 1989
+ Ty Coon, President of Vice
+
+This General Public License does not permit incorporating your program into
+proprietary programs. If your program is a subroutine library, you may
+consider it more useful to permit linking proprietary applications with the
+library. If this is what you want to do, use the GNU Library General
+Public License instead of this License.
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/Makefile b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/Makefile
new file mode 100644
index 0000000..2c23e8e
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/Makefile
@@ -0,0 +1,25 @@
+# This is a rudimentary Makefile for rebuilding Stanford CoreNLP.
+# We actually use ant (q.v.) or a Java IDE.
+
+JAVAC = javac
+JAVAFLAGS = -O -d classes -encoding utf-8
+
+# Builds the classes' jar file
+corenlp: source
+ mkdir -p classes
+ $(JAVAC) $(JAVAFLAGS) src/edu/stanford/nlp/*/*.java \
+ src/edu/stanford/nlp/*/*/*.java \
+ src/edu/stanford/nlp/*/*/*/*.java \
+ src/edu/stanford/nlp/*/*/*/*/*.java \
+ src/edu/stanford/nlp/*/*/*/*/*/*.java
+ cd classes ; jar -cfm ../stanford-corenlp-`date +%Y-%m-%d`.jar ../src/META-INF/MANIFEST.MF edu ; cd ..
+
+# Before making, unjar the source jar file in the 'src' directory
+source:
+ if [ ! -e src ] ; then \
+ mkdir src ; cd src ; jar -xf ../stanford-corenlp-*-sources.jar; \
+ fi;
+
+clean:
+ rm -rf classes
+ rm -rf src
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/README.txt b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/README.txt
new file mode 100644
index 0000000..fe34173
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/README.txt
@@ -0,0 +1,80 @@
+Stanford CoreNLP v1.3.5 - 2013-04-04
+Stanford's Suite of NLP Tools
+-----------------------------
+
+Copyright (c) 2009-2012 The Board of Trustees of
+The Leland Stanford Junior University. All Rights Reserved.
+
+DOCUMENTATION
+
+Please look at the URL below for documention for Stanford CoreNLP:
+
+ http://nlp.stanford.edu/software/corenlp.shtml
+
+LICENSE
+
+//
+// StanfordCoreNLP -- a suite of NLP tools
+// Copyright (c) 2012 The Board of Trustees of
+// The Leland Stanford Junior University. All Rights Reserved.
+//
+// This program is free software; you can redistribute it and/or
+// modify it under the terms of the GNU General Public License
+// as published by the Free Software Foundation; either version 2
+// of the License, or (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with this program; if not, write to the Free Software
+// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
+//
+// For more information, bug reports, fixes, contact:
+// Christopher Manning
+// Dept of Computer Science, Gates 1A
+// Stanford CA 94305-9010
+// USA
+//
+
+---------------------------------
+CHANGES
+---------------------------------
+
+2013-04-04 1.3.5 Speed improvements, coref improvements,
+ Chinese version, -nthreads option
+
+2012-11-12 1.3.4 Improved ner model and dependency code,
+ now possible to change annotator pool for
+ later StanfordCoreNLP objects
+
+2012-07-09 1.3.3 Minor bug fixes
+
+2012-05-22 1.3.2 Improvements to sutime
+
+2012-03-09 1.3.1 Now supports caseless models (available as DLC)
+
+2011-12-16 1.3.0 Threadsafe!
+ Bugs in time annotation fixed
+
+2011-09-14 1.2.0 Time expression recognizer added to ner annotator
+ Output bugfixes
+ Parser can now substitute for tagger
+
+2011-06-19 1.1.0 Improved coref release
+
+2011-05-15 1.0.4 More efficient dcoref data structure
+ Supports already-tokenized input text
+
+2011-04-17 1.0.3 Compatible with other releases
+ Support loading arbitrary annotators
+ Tagger bug fixes, such as "EOS" token
+
+2010-11-11 1.0.2 Remove wn.jar
+
+2010-11-11 1.0.1 Add xml removal
+
+2010-10-07 1.0 Initial release
+
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/StanfordCoreNlpDemo.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/StanfordCoreNlpDemo.java
new file mode 100644
index 0000000..1756e0b
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/StanfordCoreNlpDemo.java
@@ -0,0 +1,50 @@
+
+import java.io.*;
+import java.util.*;
+
+import edu.stanford.nlp.io.*;
+import edu.stanford.nlp.ling.*;
+import edu.stanford.nlp.pipeline.*;
+import edu.stanford.nlp.trees.*;
+import edu.stanford.nlp.util.*;
+
+public class StanfordCoreNlpDemo {
+
+ public static void main(String[] args) throws IOException {
+ PrintWriter out;
+ if (args.length > 1) {
+ out = new PrintWriter(args[1]);
+ } else {
+ out = new PrintWriter(System.out);
+ }
+ PrintWriter xmlOut = null;
+ if (args.length > 2) {
+ xmlOut = new PrintWriter(args[2]);
+ }
+
+ StanfordCoreNLP pipeline = new StanfordCoreNLP();
+ Annotation annotation;
+ if (args.length > 0) {
+ annotation = new Annotation(IOUtils.slurpFileNoExceptions(args[0]));
+ } else {
+ annotation = new Annotation("Kosgi Santosh sent an email to Stanford University. He didn't get a reply.");
+ }
+
+ pipeline.annotate(annotation);
+ pipeline.prettyPrint(annotation, out);
+ if (xmlOut != null) {
+ pipeline.xmlPrint(annotation, xmlOut);
+ }
+ // An Annotation is a Map and you can get and use the various analyses individually.
+ // For instance, this gets the parse tree of the first sentence in the text.
+ List sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
+ if (sentences != null && sentences.size() > 0) {
+ CoreMap sentence = sentences.get(0);
+ Tree tree = sentence.get(TreeCoreAnnotations.TreeAnnotation.class);
+ out.println();
+ out.println("The first sentence parsed is:");
+ tree.pennPrint(out);
+ }
+ }
+
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/build.xml b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/build.xml
new file mode 100644
index 0000000..d5c4694
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/build.xml
@@ -0,0 +1,134 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/corenlp.sh b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/corenlp.sh
new file mode 100755
index 0000000..862de38
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/corenlp.sh
@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+#
+# Runs Stanford CoreNLP.
+# Simple uses for xml and plain text output to files are:
+# ./corenlp.sh -file filename
+# ./corenlp.sh -file filename -outputFormat text
+
+scriptdir=`dirname $0`
+
+echo java -mx3g -cp \"$scriptdir/*\" edu.stanford.nlp.pipeline.StanfordCoreNLP $*
+java -mx3g -cp "$scriptdir/*" edu.stanford.nlp.pipeline.StanfordCoreNLP $*
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/input.txt b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/input.txt
new file mode 100644
index 0000000..7e2de09
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/input.txt
@@ -0,0 +1 @@
+Stanford University is located in California. It is a great university, founded in 1891.
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/input.txt.xml b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/input.txt.xml
new file mode 100644
index 0000000..63baf71
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/input.txt.xml
@@ -0,0 +1,348 @@
+
+
+
+
+
+
+
+
+ Stanford
+ Stanford
+ 0
+ 8
+ NNP
+ ORGANIZATION
+
+
+ University
+ University
+ 9
+ 19
+ NNP
+ ORGANIZATION
+
+
+ is
+ be
+ 20
+ 22
+ VBZ
+ O
+
+
+ located
+ located
+ 23
+ 30
+ JJ
+ O
+
+
+ in
+ in
+ 31
+ 33
+ IN
+ O
+
+
+ California
+ California
+ 34
+ 44
+ NNP
+ LOCATION
+
+
+ .
+ .
+ 44
+ 45
+ .
+ O
+
+
+ (ROOT (S (NP (NNP Stanford) (NNP University)) (VP (VBZ is) (ADJP (JJ located) (PP (IN in) (NP (NNP California))))) (. .)))
+
+
+ ROOT
+ located
+
+
+ University
+ Stanford
+
+
+ located
+ University
+
+
+ located
+ is
+
+
+ located
+ in
+
+
+ in
+ California
+
+
+
+
+ ROOT
+ located
+
+
+ University
+ Stanford
+
+
+ located
+ University
+
+
+ located
+ is
+
+
+ located
+ California
+
+
+
+
+ ROOT
+ located
+
+
+ University
+ Stanford
+
+
+ located
+ University
+
+
+ located
+ is
+
+
+ located
+ California
+
+
+
+
+
+
+ It
+ it
+ 46
+ 48
+ PRP
+ O
+
+
+ is
+ be
+ 49
+ 51
+ VBZ
+ O
+
+
+ a
+ a
+ 52
+ 53
+ DT
+ O
+
+
+ great
+ great
+ 54
+ 59
+ JJ
+ O
+
+
+ university
+ university
+ 60
+ 70
+ NN
+ O
+
+
+ ,
+ ,
+ 70
+ 71
+ ,
+ O
+
+
+ founded
+ found
+ 72
+ 79
+ VBN
+ O
+
+
+ in
+ in
+ 80
+ 82
+ IN
+ O
+
+
+ 1891
+ 1891
+ 83
+ 87
+ CD
+ DATE
+ 1891
+ 1891
+
+
+ .
+ .
+ 87
+ 88
+ .
+ O
+
+
+ (ROOT (S (NP (PRP It)) (VP (VBZ is) (NP (NP (DT a) (JJ great) (NN university)) (, ,) (VP (VBN founded) (PP (IN in) (NP (CD 1891)))))) (. .)))
+
+
+ ROOT
+ university
+
+
+ university
+ It
+
+
+ university
+ is
+
+
+ university
+ a
+
+
+ university
+ great
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diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/joda-time-2.1-sources.jar b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/joda-time-2.1-sources.jar
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diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/joda-time.jar b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/joda-time.jar
new file mode 100644
index 0000000..b2aca95
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diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/jollyday-0.4.7-sources.jar b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/jollyday-0.4.7-sources.jar
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diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/jollyday.jar b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/jollyday.jar
new file mode 100644
index 0000000..a6bf8b3
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diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/new_sample.txt.xml b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/new_sample.txt.xml
new file mode 100644
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+ .
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+ O
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+
+ (ROOT (S (NP (PRP It)) (VP (VBZ is) (NP (DT the) (JJ right) (NN thing) (S (VP (TO to) (VP (VB do)))))) (. .)))
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+
+
+ It
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+ is
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+
+ my
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+ intention
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+
+ proceed
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+
+ to
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+
+ the
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+
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+ NN
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+
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+
+ on
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+ or
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+
+ February
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+
+ 7
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+
+
+ ,
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+
+ as
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+
+ provided
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+ in
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+ the
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+ agreement
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+
+ we
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+ O
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+
+ last
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+ 5720
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+ DATE
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+
+
+
+ year
+ year
+ 5721
+ 5725
+ NN
+ DATE
+ THIS P1Y OFFSET P-1Y
+
+
+
+ .
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+
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+
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+
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+
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+
+ this
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+
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+
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+ 2000
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+
+
+ .
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+ .
+ O
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+
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+
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+
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+
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+ No
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+
+ single
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+ O
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+
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+
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+ more
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+ to
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+
+ our
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+ O
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+
+ future
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+ DATE
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+
+
+ economic
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+
+ securi
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+ O
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+
+ -
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+ 8869
+ 8870
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+ O
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+
+ ty
+ ty
+ 8872
+ 8874
+ NN
+ O
+
+
+ .
+ .
+ 8874
+ 8875
+ .
+ O
+
+
+ (ROOT (S (NP (DT No) (JJ single) (NN action)) (VP (VBZ is) (ADJP (RBR more) (JJ critical) (PP (TO to) (NP (PRP$ our) (JJ future) (JJ economic) (NN securi) (: -) (NN ty))))) (. .)))
+
+
+ ROOT
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+
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+
+ action
+ single
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+
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+
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+ more
+
+
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+
+ ty
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+
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+
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+
+ to
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+
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+
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+
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+
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+
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+
+
+
+
+
+ By
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+
+
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+
+ ade
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+ SYM
+ O
+
+
+ -
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+
+ quate
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+
+ our
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+
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+
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+
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+
+
+ ROOT
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+
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+
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+
+ By
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+
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+
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+
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+
+ to
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+
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+
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+
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+
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+
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+
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+
+ .
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+ O
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+
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+
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+
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+
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+
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+
+ performed
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+
+ performed
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+
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+
+ percent
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+
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+
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+
+ jobs
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+
+ percent
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+
+ jobs
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+
+ performed
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+
+ performed
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+
+ performed
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+
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+
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+
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+
+
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+ percent
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+
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+
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+
+ percent
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+
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+ performed
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+
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+
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+
+
+
+
+
+ Most
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+
+ Jobs
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+
+ will
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+
+ need
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+
+ higher
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+ O
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+
+ skills
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+ O
+
+
+ .
+ .
+ 9124
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+ .
+ O
+
+
+ (ROOT (S (NP (NP (JJS Most)) (NP (NNP Jobs))) (VP (MD will) (VP (VB need) (NP (JJR higher) (NNS skills)))) (. .)))
+
+
+ ROOT
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+
+ need
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+
+
+ Most
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+
+ need
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+
+ skills
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+
+ need
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+
+
+
+ ROOT
+ need
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+
+ need
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+
+ Most
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+
+ need
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+
+ skills
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+
+ need
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+
+
+
+ ROOT
+ need
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+
+ need
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+
+ Most
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+
+ need
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+
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+
+ need
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+
+
+
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+
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+ percent
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+
+ of
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+
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+ O
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+
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+ O
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+
+ seekers
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+ O
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+
+ at
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+
+ that
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+
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+
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+
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+
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+
+
+ ,
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+
+ immigrants
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+
+
+ ,
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+
+ and
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+
+
+ women
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+ O
+
+
+ .
+ .
+ 9216
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+ .
+ O
+
+
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+
+
+ ROOT
+ minorities
+
+
+ percent
+ Brt
+
+
+ percent
+ 80
+
+
+ minorities
+ percent
+
+
+ percent
+ of
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+
+ seekers
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+
+ seekers
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+
+
+ of
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+
+ seekers
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+
+ time
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+
+ at
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+
+ minorities
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+
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+
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+
+
+ minorities
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+
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+
+
+
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+
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+ percent
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+
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+
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+
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+
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+
+ seekers
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+
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+
+ percent
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+
+ time
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+
+ seekers
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+
+ minorities
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+
+ minorities
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+
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+
+
+ minorities
+ women
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+
+
+
+
+
+ If
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+
+ we
+ we
+ 9222
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+ O
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+
+ have
+ have
+ 9225
+ 9229
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+ O
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+
+ not
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+
+ substantially
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+ O
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+
+ improved
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+ our
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+
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+
+ levels
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+
+ by
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+
+ that
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+
+ time
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+
+ ,
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+
+ we
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+ O
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+
+ risk
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+
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+
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+
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+
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+
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+
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+
+ Square
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+ us
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+ in
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+ Manila
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+
+ .
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+ the
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+ .
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+ (ROOT (S (PP (IN In) (NP (CD 1981))) (, ,) (NP (NP (DT the) (NNP Bulletin)) (PP (IN of) (NP (JJ Atomic) (NNS Scientists)))) (VP (VBD advanced) (NP (NP (DT the) (NN clock)) (PP (IN of) (NP (JJ global) (NN survival)))) (PP (TO to) (NP (CD three) (NNS minutes))) (PP (IN before) (NP (NP (NP (DT the) (NN midnight)) (PP (IN of) (NP (NN nucle)))) (: -) (NP (NP (NN ar)) (NP (NNP Armageddon)))))) (. .)))
+
+
+ ROOT
+ advanced
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+
+ advanced
+ In
+
+
+ In
+ 1981
+
+
+ Bulletin
+ the
+
+
+ advanced
+ Bulletin
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+
+ Bulletin
+ of
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+
+ Scientists
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+ of
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+
+ clock
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+ advanced
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+
+ clock
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+
+ survival
+ global
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+
+ of
+ survival
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+
+ advanced
+ to
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+
+ minutes
+ three
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+
+ to
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+
+
+ advanced
+ before
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+
+ midnight
+ the
+
+
+ before
+ midnight
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+
+ midnight
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+
+ of
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+ midnight
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+
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+ ROOT
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+
+ Bulletin
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+ advanced
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+ midnight
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+
+ advanced
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+
+ midnight
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+ Today
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+ 22801
+ 22805
+ NNP
+ O
+
+
+ ,
+ ,
+ 22805
+ 22806
+ ,
+ O
+
+
+ that
+ that
+ 22807
+ 22811
+ DT
+ O
+
+
+ is
+ be
+ 22812
+ 22814
+ VBZ
+ O
+
+
+ ,
+ ,
+ 22814
+ 22815
+ ,
+ O
+
+
+ the
+ the
+ 22816
+ 22819
+ DT
+ O
+
+
+ Third
+ Third
+ 22820
+ 22825
+ NNP
+ ORDINAL
+ 3.0
+
+
+ World
+ World
+ 22829
+ 22834
+ NNP
+ MISC
+
+
+ ,
+ ,
+ 22834
+ 22835
+ ,
+ O
+
+
+ the
+ the
+ 22836
+ 22839
+ DT
+ O
+
+
+ developing
+ develop
+ 22840
+ 22850
+ VBG
+ O
+
+
+ world
+ world
+ 22851
+ 22856
+ NN
+ O
+
+
+ which
+ which
+ 22857
+ 22862
+ WDT
+ O
+
+
+ is
+ be
+ 22863
+ 22865
+ VBZ
+ O
+
+
+ home
+ home
+ 22866
+ 22870
+ NN
+ O
+
+
+ to
+ to
+ 22874
+ 22876
+ TO
+ O
+
+
+ 70
+ 70
+ 22877
+ 22879
+ CD
+ PERCENT
+ %70.0
+
+
+ percent
+ percent
+ 22880
+ 22887
+ NN
+ PERCENT
+ %70.0
+
+
+ of
+ of
+ 22888
+ 22890
+ IN
+ O
+
+
+ the
+ the
+ 22891
+ 22894
+ DT
+ O
+
+
+ world
+ world
+ 22895
+ 22900
+ NN
+ O
+
+
+ 's
+ 's
+ 22900
+ 22902
+ POS
+ O
+
+
+ population
+ population
+ 22903
+ 22913
+ NN
+ O
+
+
+ .
+ .
+ 22913
+ 22914
+ .
+ O
+
+
+ (ROOT (S (ADVP (RB However)) (, ,) (NP (PRP we)) (VP (MD can) (RB not) (VP (VB be) (ADJP (ADJP (JJ indifferent) (PP (TO to) (NP (NP (DT an) (JJ environmental) (NN deterio)) (: -) (NP (NP (NN ration)) (PP (IN in) (NP (NP (DT that) (NN part)) (PP (IN of) (NP (NP (DT the) (NN world)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is)))))))))))) (SBAR (X (SYM nei)) (S (: -) (PP (IN ther) (NP (NNP East) (CC nor) (NNP West)) (, ,)) (NP (DT that)) (VP (VBZ is) (, ,) (FRAG (NP (NP (DT the) (NNP Third) (NNP World)) (, ,) (NP (NP (DT the) (VBG developing) (NN world)) (SBAR (WHNP (WDT which)) (S (VP (VBZ is) (ADVP (NN home)) (PP (TO to) (NP (NP (CD 70) (NN percent)) (PP (IN of) (NP (NP (DT the) (NN world) (POS 's)) (NN population))))))))))))))))) (. .)))
+
+
+ ROOT
+ indifferent
+
+
+ indifferent
+ However
+
+
+ indifferent
+ we
+
+
+ indifferent
+ can
+
+
+ indifferent
+ not
+
+
+ indifferent
+ be
+
+
+ indifferent
+ to
+
+
+ deterio
+ an
+
+
+ deterio
+ environmental
+
+
+ to
+ deterio
+
+
+ deterio
+ ration
+
+
+ ration
+ in
+
+
+ part
+ that
+
+
+ in
+ part
+
+
+ part
+ of
+
+
+ world
+ the
+
+
+ of
+ world
+
+
+ is
+ which
+
+
+ world
+ is
+
+
+ is
+ nei
+
+
+ is
+ ther
+
+
+ ther
+ East
+
+
+ East
+ nor
+
+
+ East
+ West
+
+
+ is
+ that
+
+
+ indifferent
+ is
+
+
+ World
+ the
+
+
+ World
+ Third
+
+
+ is
+ World
+
+
+ world
+ the
+
+
+ world
+ developing
+
+
+ World
+ world
+
+
+ is
+ which
+
+
+ world
+ is
+
+
+ is
+ home
+
+
+ is
+ to
+
+
+ percent
+ 70
+
+
+ to
+ percent
+
+
+ percent
+ of
+
+
+ world
+ the
+
+
+ population
+ world
+
+
+ world
+ 's
+
+
+ of
+ population
+
+
+
+
+ ROOT
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+
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+ indifferent
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+
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+
+ indifferent
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+ deterio
+ an
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+
+ deterio
+ environmental
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+
+ indifferent
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+
+ deterio
+ ration
+
+
+ part
+ that
+
+
+ ration
+ part
+
+
+ world
+ the
+
+
+ part
+ world
+
+
+ is
+ which
+
+
+ world
+ is
+
+
+ is
+ nei
+
+
+ is
+ East
+
+
+ East
+ West
+
+
+ is
+ that
+
+
+ indifferent
+ is
+
+
+ World
+ the
+
+
+ World
+ Third
+
+
+ is
+ World
+
+
+ world
+ the
+
+
+ world
+ developing
+
+
+ World
+ world
+
+
+ is
+ which
+
+
+ world
+ is
+
+
+ is
+ home
+
+
+ percent
+ 70
+
+
+ is
+ percent
+
+
+ world
+ the
+
+
+ population
+ world
+
+
+ percent
+ population
+
+
+
+
+ ROOT
+ indifferent
+
+
+ indifferent
+ However
+
+
+ indifferent
+ we
+
+
+ indifferent
+ can
+
+
+ indifferent
+ not
+
+
+ indifferent
+ be
+
+
+ deterio
+ an
+
+
+ deterio
+ environmental
+
+
+ indifferent
+ deterio
+
+
+ deterio
+ ration
+
+
+ part
+ that
+
+
+ ration
+ part
+
+
+ world
+ the
+
+
+ part
+ world
+
+
+ is
+ which
+
+
+ world
+ is
+
+
+ is
+ nei
+
+
+ is
+ East
+
+
+ East
+ West
+
+
+ is
+ West
+
+
+ is
+ that
+
+
+ indifferent
+ is
+
+
+ World
+ the
+
+
+ World
+ Third
+
+
+ is
+ World
+
+
+ world
+ the
+
+
+ world
+ developing
+
+
+ World
+ world
+
+
+ is
+ which
+
+
+ world
+ is
+
+
+ is
+ home
+
+
+ percent
+ 70
+
+
+ is
+ percent
+
+
+ world
+ the
+
+
+ population
+ world
+
+
+ percent
+ population
+
+
+
+
+
+
+ On
+ on
+ 22920
+ 22922
+ IN
+ O
+
+
+ an
+ a
+ 22923
+ 22925
+ DT
+ O
+
+
+ environmental
+ environmental
+ 22926
+ 22939
+ JJ
+ O
+
+
+ level
+ level
+ 22940
+ 22945
+ NN
+ O
+
+
+ ,
+ ,
+ 22945
+ 22946
+ ,
+ O
+
+
+ we
+ we
+ 22947
+ 22949
+ PRP
+ O
+
+
+ will
+ will
+ 22950
+ 22954
+ MD
+ O
+
+
+ accom
+ accom
+ 22955
+ 22960
+ VB
+ O
+
+
+ -
+ -
+ 22960
+ 22961
+ :
+ O
+
+
+ plish
+ plish
+ 22963
+ 22968
+ NN
+ O
+
+
+ little
+ little
+ 22969
+ 22975
+ RB
+ O
+
+
+ if
+ if
+ 22976
+ 22978
+ IN
+ O
+
+
+ the
+ the
+ 22979
+ 22982
+ DT
+ O
+
+
+ savings
+ savings
+ 22983
+ 22990
+ NNS
+ O
+
+
+ in
+ in
+ 22991
+ 22993
+ IN
+ O
+
+
+ greenhouse
+ greenhouse
+ 22994
+ 23004
+ NN
+ O
+
+
+ gases
+ gas
+ 23006
+ 23011
+ NNS
+ O
+
+
+ made
+ make
+ 23012
+ 23016
+ VBN
+ O
+
+
+ by
+ by
+ 23017
+ 23019
+ IN
+ O
+
+
+ conservation
+ conservation
+ 23020
+ 23032
+ NN
+ O
+
+
+ and
+ and
+ 23033
+ 23036
+ CC
+ O
+
+
+ new
+ new
+ 23037
+ 23040
+ JJ
+ O
+
+
+ tech
+ tech
+ 23041
+ 23045
+ NN
+ O
+
+
+ -
+ -
+ 23045
+ 23046
+ :
+ O
+
+
+ nology
+ nology
+ 23048
+ 23054
+ NN
+ O
+
+
+ use
+ use
+ 23055
+ 23058
+ NN
+ O
+
+
+ In
+ in
+ 23059
+ 23061
+ IN
+ O
+
+
+ the
+ the
+ 23062
+ 23065
+ DT
+ O
+
+
+ developed
+ developed
+ 23066
+ 23075
+ JJ
+ O
+
+
+ countries
+ country
+ 23076
+ 23085
+ NNS
+ O
+
+
+ are
+ be
+ 23086
+ 23089
+ VBP
+ O
+
+
+ offset
+ offset
+ 23091
+ 23097
+ VBN
+ O
+
+
+ by
+ by
+ 23098
+ 23100
+ IN
+ O
+
+
+ the
+ the
+ 23101
+ 23104
+ DT
+ O
+
+
+ ecologically
+ ecologically
+ 23105
+ 23117
+ RB
+ O
+
+
+ unsound
+ unsound
+ 23118
+ 23125
+ JJ
+ O
+
+
+ Industri
+ Industri
+ 23126
+ 23134
+ NNP
+ ORGANIZATION
+
+
+ -
+ -
+ 23134
+ 23135
+ :
+ O
+
+
+ alization
+ alization
+ 23137
+ 23146
+ NN
+ O
+
+
+ of
+ of
+ 23147
+ 23149
+ IN
+ O
+
+
+ the
+ the
+ 23150
+ 23153
+ DT
+ O
+
+
+ developing
+ develop
+ 23154
+ 23164
+ VBG
+ O
+
+
+ world
+ world
+ 23165
+ 23170
+ NN
+ O
+
+
+ and
+ and
+ 23171
+ 23174
+ CC
+ O
+
+
+ by
+ by
+ 23175
+ 23177
+ IN
+ O
+
+
+ the
+ the
+ 23178
+ 23181
+ DT
+ O
+
+
+ destruction
+ destruction
+ 23183
+ 23194
+ NN
+ O
+
+
+ of
+ of
+ 23195
+ 23197
+ IN
+ O
+
+
+ the
+ the
+ 23198
+ 23201
+ DT
+ O
+
+
+ tropical
+ tropical
+ 23202
+ 23210
+ JJ
+ O
+
+
+ forests
+ forest
+ 23211
+ 23218
+ NNS
+ O
+
+
+ which
+ which
+ 23219
+ 23224
+ WDT
+ O
+
+
+ are
+ be
+ 23225
+ 23228
+ VBP
+ O
+
+
+ quite
+ quite
+ 23230
+ 23235
+ RB
+ O
+
+
+ literally
+ literally
+ 23236
+ 23245
+ RB
+ O
+
+
+ the
+ the
+ 23246
+ 23249
+ DT
+ O
+
+
+ lungs
+ lung
+ 23250
+ 23255
+ NNS
+ O
+
+
+ of
+ of
+ 23256
+ 23258
+ IN
+ O
+
+
+ our
+ we
+ 23259
+ 23262
+ PRP$
+ O
+
+
+ planet
+ planet
+ 23263
+ 23269
+ NN
+ O
+
+
+ .
+ .
+ 23269
+ 23270
+ .
+ O
+
+
+ (ROOT (S (PP (IN On) (NP (DT an) (JJ environmental) (NN level))) (, ,) (NP (PRP we)) (VP (MD will) (VP (VB accom) (: -) (S (NP (NP (NN plish) (RB little)) (SBAR (IN if) (S (NP (NP (DT the) (NNS savings)) (PP (IN in) (NP (NN greenhouse) (NNS gases)))) (VP (VBN made) (PP (IN by) (NP (NP (NN conservation)) (CC and) (NP (JJ new) (NN tech)))))))) (: -) (NP (NN nology) (NN use)) (PP (IN In) (NP (DT the) (JJ developed) (NNS countries))) (VP (VBP are) (VP (VBN offset) (PP (PP (IN by) (NP (NP (DT the) (ADJP (RB ecologically) (JJ unsound)) (NNP Industri)) (: -) (NP (NP (NN alization)) (PP (IN of) (NP (DT the) (VBG developing) (NN world)))))) (CC and) (PP (IN by) (NP (NP (DT the) (NN destruction)) (PP (IN of) (NP (NP (DT the) (JJ tropical) (NNS forests)) (SBAR (WHNP (WDT which)) (S (VP (VBP are) (ADVP (RB quite) (RB literally)) (NP (NP (DT the) (NNS lungs)) (PP (IN of) (NP (PRP$ our) (NN planet))))))))))))))))) (. .)))
+
+
+ ROOT
+ accom
+
+
+ accom
+ On
+
+
+ level
+ an
+
+
+ level
+ environmental
+
+
+ On
+ level
+
+
+ accom
+ we
+
+
+ accom
+ will
+
+
+ offset
+ plish
+
+
+ plish
+ little
+
+
+ made
+ if
+
+
+ savings
+ the
+
+
+ made
+ savings
+
+
+ savings
+ in
+
+
+ gases
+ greenhouse
+
+
+ in
+ gases
+
+
+ plish
+ made
+
+
+ made
+ by
+
+
+ by
+ conservation
+
+
+ conservation
+ and
+
+
+ tech
+ new
+
+
+ conservation
+ tech
+
+
+ use
+ nology
+
+
+ offset
+ use
+
+
+ offset
+ In
+
+
+ countries
+ the
+
+
+ countries
+ developed
+
+
+ In
+ countries
+
+
+ offset
+ are
+
+
+ accom
+ offset
+
+
+ offset
+ by
+
+
+ Industri
+ the
+
+
+ unsound
+ ecologically
+
+
+ Industri
+ unsound
+
+
+ by
+ Industri
+
+
+ Industri
+ alization
+
+
+ alization
+ of
+
+
+ world
+ the
+
+
+ world
+ developing
+
+
+ of
+ world
+
+
+ by
+ and
+
+
+ by
+ by
+
+
+ destruction
+ the
+
+
+ by
+ destruction
+
+
+ destruction
+ of
+
+
+ forests
+ the
+
+
+ forests
+ tropical
+
+
+ of
+ forests
+
+
+ lungs
+ which
+
+
+ lungs
+ are
+
+
+ literally
+ quite
+
+
+ lungs
+ literally
+
+
+ lungs
+ the
+
+
+ forests
+ lungs
+
+
+ lungs
+ of
+
+
+ planet
+ our
+
+
+ of
+ planet
+
+
+
+
+ ROOT
+ accom
+
+
+ level
+ an
+
+
+ level
+ environmental
+
+
+ accom
+ level
+
+
+ accom
+ we
+
+
+ accom
+ will
+
+
+ offset
+ plish
+
+
+ plish
+ little
+
+
+ made
+ if
+
+
+ savings
+ the
+
+
+ made
+ savings
+
+
+ gases
+ greenhouse
+
+
+ savings
+ gases
+
+
+ plish
+ made
+
+
+ made
+ conservation
+
+
+ tech
+ new
+
+
+ conservation
+ tech
+
+
+ use
+ nology
+
+
+ offset
+ use
+
+
+ countries
+ the
+
+
+ countries
+ developed
+
+
+ offset
+ countries
+
+
+ offset
+ are
+
+
+ accom
+ offset
+
+
+ Industri
+ the
+
+
+ unsound
+ ecologically
+
+
+ Industri
+ unsound
+
+
+ offset
+ Industri
+
+
+ Industri
+ alization
+
+
+ world
+ the
+
+
+ world
+ developing
+
+
+ alization
+ world
+
+
+ destruction
+ the
+
+
+ Industri
+ destruction
+
+
+ forests
+ the
+
+
+ forests
+ tropical
+
+
+ destruction
+ forests
+
+
+ lungs
+ which
+
+
+ lungs
+ are
+
+
+ literally
+ quite
+
+
+ lungs
+ literally
+
+
+ lungs
+ the
+
+
+ forests
+ lungs
+
+
+ planet
+ our
+
+
+ lungs
+ planet
+
+
+
+
+ ROOT
+ accom
+
+
+ level
+ an
+
+
+ level
+ environmental
+
+
+ accom
+ level
+
+
+ accom
+ we
+
+
+ accom
+ will
+
+
+ offset
+ plish
+
+
+ plish
+ little
+
+
+ made
+ if
+
+
+ savings
+ the
+
+
+ made
+ savings
+
+
+ gases
+ greenhouse
+
+
+ savings
+ gases
+
+
+ plish
+ made
+
+
+ made
+ conservation
+
+
+ tech
+ new
+
+
+ made
+ tech
+
+
+ conservation
+ tech
+
+
+ use
+ nology
+
+
+ offset
+ use
+
+
+ countries
+ the
+
+
+ countries
+ developed
+
+
+ offset
+ countries
+
+
+ offset
+ are
+
+
+ accom
+ offset
+
+
+ Industri
+ the
+
+
+ unsound
+ ecologically
+
+
+ Industri
+ unsound
+
+
+ offset
+ Industri
+
+
+ Industri
+ alization
+
+
+ world
+ the
+
+
+ world
+ developing
+
+
+ alization
+ world
+
+
+ destruction
+ the
+
+
+ offset
+ destruction
+
+
+ Industri
+ destruction
+
+
+ forests
+ the
+
+
+ forests
+ tropical
+
+
+ destruction
+ forests
+
+
+ lungs
+ which
+
+
+ lungs
+ are
+
+
+ literally
+ quite
+
+
+ lungs
+ literally
+
+
+ lungs
+ the
+
+
+ forests
+ lungs
+
+
+ planet
+ our
+
+
+ lungs
+ planet
+
+
+
+
+
+
+ Worse
+ worse
+ 23272
+ 23277
+ RBR
+ O
+
+
+ ,
+ ,
+ 23277
+ 23278
+ ,
+ O
+
+
+ environmental
+ environmental
+ 23279
+ 23292
+ JJ
+ O
+
+
+ degradation
+ degradation
+ 23293
+ 23304
+ NN
+ O
+
+
+ in
+ in
+ 23305
+ 23307
+ IN
+ O
+
+
+ the
+ the
+ 23308
+ 23311
+ DT
+ O
+
+
+ third
+ third
+ 23313
+ 23318
+ JJ
+ ORDINAL
+ 3.0
+
+
+ world
+ world
+ 23319
+ 23324
+ NN
+ O
+
+
+ is
+ be
+ 23325
+ 23327
+ VBZ
+ O
+
+
+ the
+ the
+ 23328
+ 23331
+ DT
+ O
+
+
+ product
+ product
+ 23332
+ 23339
+ NN
+ O
+
+
+ of
+ of
+ 23340
+ 23342
+ IN
+ O
+
+
+ ,
+ ,
+ 23342
+ 23343
+ ,
+ O
+
+
+ the
+ the
+ 23344
+ 23347
+ DT
+ O
+
+
+ compan
+ compan
+ 23348
+ 23354
+ NN
+ O
+
+
+ -
+ -
+ 23354
+ 23355
+ :
+ O
+
+
+ ion
+ ion
+ 23357
+ 23360
+ NN
+ O
+
+
+ of
+ of
+ 23361
+ 23363
+ IN
+ O
+
+
+ ,
+ ,
+ 23363
+ 23364
+ ,
+ O
+
+
+ and
+ and
+ 23365
+ 23368
+ CC
+ O
+
+
+ the
+ the
+ 23369
+ 23372
+ DT
+ O
+
+
+ cause
+ cause
+ 23373
+ 23378
+ NN
+ O
+
+
+ of
+ of
+ 23379
+ 23381
+ IN
+ O
+
+
+ increased
+ increase
+ 23382
+ 23391
+ VBN
+ O
+
+
+ poverty
+ poverty
+ 23392
+ 23399
+ NN
+ O
+
+
+ and
+ and
+ 23401
+ 23404
+ CC
+ O
+
+
+ human
+ human
+ 23405
+ 23410
+ JJ
+ O
+
+
+ misery
+ misery
+ 23411
+ 23417
+ NN
+ O
+
+
+ .
+ .
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+ O
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+ O
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+
+ .
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+ O
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+
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+
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+ ,
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+ our
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+ .
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+ (ROOT (S (PP (VBN Given) (NP (DT the) (NNS consequences))) (, ,) (NP (NP (PRP$ our) (NN response)) (PP (TO to) (NP (NP (JJ environmental) (NN deterioration)) (PP (IN in) (NP (DT the) (JJ third) (NN world)))))) (VP (VBZ is) (ADJP (RB woefully) (JJ inadequate))) (. .)))
+
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+ ROOT
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+
+ Indeed
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+
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+
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+ -
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+
+ quences
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+ O
+
+
+ .
+ .
+ 24177
+ 24178
+ .
+ O
+
+
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+ Indeed
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+
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+
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+
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+
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+
+ conse
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+
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+
+
+ It
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+
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+ O
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+
+ only
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+ 24193
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+
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+
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+
+ that
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+
+ the
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+
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+
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+
+ spe
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+ 24264
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+ O
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+
+ -
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+ O
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+
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+ 24280
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+ O
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+
+ with
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+
+ the
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+ 24295
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+
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+
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+
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+ O
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+
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+
+ ROOT
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+
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+
+
+
+ ROOT
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+
+
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+
+
+
+
+
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+
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+ ,
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+
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+
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+
+ ,
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+
+ remains
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+
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+ 24392
+ 24396
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+
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+
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+
+ .
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+ .
+ O
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+
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+
+
+ ROOT
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+
+ organization
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+
+ Na
+ the
+
+
+ Na
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+
+ organization
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+
+ Na
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+
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+
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+
+ Na
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+ organization
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+
+ Na
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+
+ Program
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+
+ Na
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+
+ Na
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+
+ organization
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+
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+
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+
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+
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+
+
+
+
+
+ In
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+
+ 1989
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+ 24426
+ CD
+ DATE
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+ the
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+
+ UNEP
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+
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+
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+ $
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+ ,
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+
+ even
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+
+ one
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+
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+ PERCENT
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+
+ of
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+
+ U.S.
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+
+ environmental
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+
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+
+ at
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+ 24527
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+
+
+ the
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+ 24528
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+
+ federal
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+ O
+
+
+ level
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+ 24540
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+ O
+
+
+ .
+ .
+ 24545
+ 24546
+ .
+ O
+
+
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+
+
+ ROOT
+ expenditure
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+
+ expenditure
+ In
+
+
+ In
+ 1989
+
+
+ budget
+ the
+
+
+ budget
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+
+ expenditure
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+ mere
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+ $
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+ percent
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+ expenditure
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+
+ percent
+ of
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+
+ of
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+ level
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+
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+
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+ 17
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+ has
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+
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+
+ an
+ a
+ 24577
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+
+ extraordinary
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+
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+
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+ in
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+
+ developing
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+ in
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+ assisting
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+
+ course
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+
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+
+ focused
+ heavily
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+
+ Issue
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+
+
+ focused
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+
+
+ Issue
+ resources
+
+
+
+
+
+
+ After
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+
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+
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+
+ borrowing
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+
+ and
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+
+ spending
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+ 25862
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+ O
+
+
+ ,
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+
+ it
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+
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+
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+
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+
+ in
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+
+ the
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+
+ United
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+
+
+ States
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+
+ to
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+ 25915
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+
+
+ talk
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+ O
+
+
+ of
+ of
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+
+ actions
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+
+ that
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+
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+
+ not
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+
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+
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+
+ .
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+
+ (ROOT (S (PP (IN After) (NP (NP (DT a) (NN decade)) (PP (IN of) (NP (NN borrowing) (CC and) (NN spending))))) (, ,) (NP (PRP it)) (VP (VBZ has) (VP (VBN become) (S (ADJP (JJ fashionable))) (PP (IN in) (NP (DT the) (NNP United) (NNPS States))) (VP (TO to) (VP (VB talk) (PP (IN of) (NP (NP (NNS actions)) (SBAR (WHNP (WDT that)) (S (VP (VBP do) (RB not) (VP (VB cost) (NP (NN money)))))))))))) (. .)))
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+
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+
+ After
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+
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+
+ of
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+
+ borrowing
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+
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+
+ States
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+
+ States
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+
+ in
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+
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+ of
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+
+ of
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+
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+
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+
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+ talk
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+
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+
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+
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+
+ cost
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+
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+
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+
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+
+ decade
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+
+ decade
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+
+ borrowing
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+
+ talk
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+
+ talk
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+
+ talk
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+
+ talk
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+
+ States
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+
+ States
+ United
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+
+ talk
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+
+
+ talk
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+
+ talk
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+
+ cost
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+
+ cost
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+
+ cost
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+
+ actions
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+
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+
+
+
+
+ Given
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+
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+
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+
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+
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+
+ the
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+ O
+
+
+ East
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+ NNP
+ LOCATION
+
+
+ ,
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+
+ they
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+
+ too
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+
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+
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+
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+
+ the
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+
+ same
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+
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+
+
+ .
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+ .
+ O
+
+
+ (ROOT (S (PP (VBN Given) (NP (NP (DT the) (JJ economic) (NN crisis)) (PP (IN of) (NP (DT the) (NNP East))))) (, ,) (NP (PRP they)) (ADVP (RB too)) (VP (MD may) (VP (VB be) (ADJP (JJ subject) (PP (TO to) (NP (DT the) (JJ same) (NN tendency)))))) (. .)))
+
+
+ ROOT
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+
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+ Given
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+
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+
+ crisis
+ economic
+
+
+ Given
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+
+ crisis
+ of
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+
+ East
+ the
+
+
+ of
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+
+ subject
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+
+ subject
+ too
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+
+ subject
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+
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+
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+
+ tendency
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+
+ tendency
+ same
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+
+ to
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+
+
+
+ ROOT
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+
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+ Given
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+
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+
+ crisis
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+
+ Given
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+
+ East
+ the
+
+
+ crisis
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+
+ subject
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+
+ subject
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+
+ subject
+ may
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+
+ subject
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+
+ tendency
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+
+ tendency
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+
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+
+
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+ ROOT
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+
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+
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+
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+
+ Given
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+
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+
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+
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+
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+
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+
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+
+
+ And
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+
+ there
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+
+ is
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+
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+
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+
+ course
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+
+ ,
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+
+ much
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+
+ that
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+
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+
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+
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+
+ to
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+
+
+ protect
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+
+ the
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+
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+
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+
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+
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+
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+
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+
+
+ .
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+ 26153
+ .
+ O
+
+
+ (ROOT (S (CC And) (NP (EX there)) (VP (VBZ is) (, ,) (PP (IN of) (NP (NN course))) (, ,) (SBAR (RB much) (IN that) (S (VP (MD can) (VP (VB be) (VP (VBN done) (S (VP (TO to) (VP (VB protect) (NP (DT the) (NN environment)) (PP (IN without) (S (VP (VBG costing) (NP (NP (DT a) (NN lot)) (PP (IN of) (NP (NN money)))))))))))))))) (. .)))
+
+
+ ROOT
+ is
+
+
+ is
+ And
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+
+ is
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+
+ is
+ of
+
+
+ of
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+
+ done
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+
+ is
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+
+ is
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+
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+
+ lot
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+ costing
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+
+ lot
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+
+
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+ ROOT
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+ is
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+
+ is
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+ is
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+
+
+
+
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+ However
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+
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+
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+ ROOT
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+
+ address
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+ for
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+
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+
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+
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+ 1990
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+ peoples
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+ to
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+ re
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+ .
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+ O
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+ 149
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+ 1
+ 2
+ 1
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+ 3
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+ 162
+ 1
+ 2
+ 1
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+
+
+
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+
+
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+
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+
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+
+
+ 173
+ 1
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+ 162
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+
+ 162
+ 9
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+
+
+
+
+ 152
+ 1
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+ 2
+
+
+ 179
+ 6
+ 12
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+
+
+ 183
+ 14
+ 20
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+ 191
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+
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+ 4
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+
+
+ 155
+ 10
+ 13
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+
+
+ 179
+ 9
+ 12
+ 11
+
+
+ 183
+ 17
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+
+
+ 185
+ 12
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+
+
+ 191
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+
+
+
+
+ 150
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+ 18
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+
+
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+
+
+
+
+ 157
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+
+ 157
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+
+ 157
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+
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+
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+
+
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+
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+
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+
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+
+
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+
+
+ 164
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+ 41
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+
+
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+
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+
+
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+
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+ 2
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+
+
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+ 33
+
+
+
+
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+
+
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+
+
+
+
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+ 4
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+
+
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+
+
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+
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+ 37
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+
+
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+ 7
+ 8
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+
+
+ 182
+ 23
+ 24
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+
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+ 1
+ 2
+ 1
+
+
+ 185
+ 19
+ 20
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+
+
+ 185
+ 23
+ 24
+ 23
+
+
+ 186
+ 3
+ 4
+ 3
+
+
+ 187
+ 6
+ 7
+ 6
+
+
+ 187
+ 59
+ 60
+ 59
+
+
+
+
+ 175
+ 5
+ 13
+ 6
+
+
+ 177
+ 5
+ 7
+ 6
+
+
+
+
+ 220
+ 3
+ 4
+ 3
+
+
+ 176
+ 15
+ 20
+ 16
+
+
+
+
+ 221
+ 41
+ 46
+ 45
+
+
+ 196
+ 1
+ 12
+ 2
+
+
+ 196
+ 1
+ 3
+ 2
+
+
+ 196
+ 4
+ 11
+ 6
+
+
+ 196
+ 9
+ 11
+ 10
+
+
+ 198
+ 5
+ 6
+ 5
+
+
+ 199
+ 2
+ 4
+ 2
+
+
+ 200
+ 19
+ 20
+ 19
+
+
+ 202
+ 9
+ 10
+ 9
+
+
+ 223
+ 11
+ 12
+ 11
+
+
+ 224
+ 3
+ 5
+ 3
+
+
+ 239
+ 5
+ 7
+ 5
+
+
+ 240
+ 10
+ 11
+ 10
+
+
+
+
+ 194
+ 15
+ 20
+ 15
+
+
+ 194
+ 21
+ 22
+ 21
+
+
+
+
+ 235
+ 24
+ 27
+ 26
+
+
+ 193
+ 17
+ 22
+ 17
+
+
+
+
+ 194
+ 11
+ 22
+ 13
+
+
+ 195
+ 1
+ 2
+ 1
+
+
+
+
+ 189
+ 1
+ 3
+ 2
+
+
+ 190
+ 1
+ 2
+ 1
+
+
+
+
+ 188
+ 10
+ 29
+ 11
+
+
+ 188
+ 10
+ 13
+ 11
+
+
+ 188
+ 14
+ 19
+ 15
+
+
+ 188
+ 21
+ 29
+ 22
+
+
+
+
+ 187
+ 28
+ 31
+ 30
+
+
+ 198
+ 21
+ 22
+ 21
+
+
+ 241
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+ 29
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+
+
+
+
+ 206
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+
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+
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+
+
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+
+
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+
+
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+
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+
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+
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+
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+
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+ 1
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+
+
+
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+
+
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+
+
+
+
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+
+
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+ 3
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+
+
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+ 15
+ 16
+ 15
+
+
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+ 18
+ 19
+ 18
+
+
+
+
+ 199
+ 10
+ 38
+ 11
+
+
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+ 10
+ 16
+ 11
+
+
+ 199
+ 17
+ 38
+ 21
+
+
+
+
+ 197
+ 2
+ 3
+ 2
+
+
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+ 2
+ 3
+ 2
+
+
+
+
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+
+
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+ 3
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+ 5
+
+
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+ 8
+ 11
+ 10
+
+
+
+
+
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/pom.xml b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/pom.xml
new file mode 100644
index 0000000..0a7d6e8
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/pom.xml
@@ -0,0 +1,82 @@
+
+ 4.0.0
+ edu.stanford.nlp
+ stanford-corenlp
+ 1.3.5
+ jar
+ Stanford CoreNLP
+ Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.
+ http://nlp.stanford.edu/software/corenlp.shtml
+
+
+ GNU General Public License Version 2
+ http://www.gnu.org/licenses/gpl-2.0.txt
+
+
+
+ http://nlp.stanford.edu/software/stanford-corenlp-2013-04-04.tgz
+ http://nlp.stanford.edu/software/stanford-corenlp-2013-04-04.tgz
+
+
+
+ christopher.manning
+ Christopher Manning
+ manning@stanford.edu
+
+
+ john.bauer
+ John Bauer
+ horatio@gmail.com
+
+
+
+ 1.6
+ 1.6
+ UTF-8
+
+
+
+ xom
+ xom
+ 1.2.5
+
+
+ joda-time
+ joda-time
+ 2.1
+
+
+ de.jollyday
+ jollyday
+ 0.4.7
+
+
+
+ src
+
+
+ org.codehaus.mojo
+ build-helper-maven-plugin
+ 1.7
+
+
+ attach-models
+ package
+
+ attach-artifact
+
+
+
+
+ ${project.basedir}/stanford-corenlp-1.3.5-models.jar
+ jar
+ models
+
+
+
+
+
+
+
+
+
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/META-INF/MANIFEST.MF b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/META-INF/MANIFEST.MF
new file mode 100644
index 0000000..0a188a0
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/META-INF/MANIFEST.MF
@@ -0,0 +1,6 @@
+Manifest-Version: 1.0
+Implementation-Version: 1.3.5
+Created-By: Stanford JavaNLP (horatio)
+Main-class: edu.stanford.nlp.pipeline.StanfordCoreNLP
+Built-Date: 2013-04-04
+
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/AbstractLinearClassifierFactory.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/AbstractLinearClassifierFactory.java
new file mode 100644
index 0000000..dd00e2f
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/AbstractLinearClassifierFactory.java
@@ -0,0 +1,91 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.ling.Datum;
+import edu.stanford.nlp.ling.RVFDatum;
+import edu.stanford.nlp.util.Index;
+import edu.stanford.nlp.util.HashIndex;
+
+import java.lang.ref.Reference;
+import java.util.Collection;
+import java.util.List;
+
+/**
+ * Shared methods for training a {@link LinearClassifier}.
+ * Inheriting classes need to implement the
+ * trainWeights method.
+ *
+ * @author Dan Klein
+ *
+ * @author Sarah Spikes (sdspikes@cs.stanford.edu) (Templatization)
+ *
+ * @param The type of the labels in the Dataset and Datum
+ * @param The type of the features in the Dataset and Datum
+ */
+
+public abstract class AbstractLinearClassifierFactory implements ClassifierFactory> {
+
+ private static final long serialVersionUID = 1L;
+
+ Index labelIndex = new HashIndex();
+ Index featureIndex = new HashIndex();
+
+ public AbstractLinearClassifierFactory() {
+ }
+
+ int numFeatures() {
+ return featureIndex.size();
+ }
+
+ int numClasses() {
+ return labelIndex.size();
+ }
+
+ public Classifier trainClassifier(List> examples) {
+ Dataset dataset = new Dataset();
+ dataset.addAll(examples);
+ return trainClassifier(dataset);
+ }
+
+ protected abstract double[][] trainWeights(GeneralDataset dataset) ;
+
+ /**
+ * Takes a {@link Collection} of {@link Datum} objects and gives you back a
+ * {@link Classifier} trained on it.
+ *
+ * @param examples {@link Collection} of {@link Datum} objects to train the
+ * classifier on
+ * @return A {@link Classifier} trained on it.
+ */
+ public LinearClassifier trainClassifier(Collection> examples) {
+ Dataset dataset = new Dataset();
+ dataset.addAll(examples);
+ return trainClassifier(dataset);
+ }
+
+ /**
+ * Takes a {@link Reference} to a {@link Collection} of {@link Datum}
+ * objects and gives you back a {@link Classifier} trained on them
+ *
+ * @param ref {@link Reference} to a {@link Collection} of {@link
+ * Datum} objects to train the classifier on
+ * @return A Classifier trained on a collection of Datum
+ */
+ public LinearClassifier trainClassifier(Reference>> ref) {
+ Collection> examples = ref.get();
+ return trainClassifier(examples);
+ }
+
+
+ /**
+ * Trains a {@link Classifier} on a {@link Dataset}.
+ *
+ * @return A {@link Classifier} trained on the data.
+ */
+ public LinearClassifier trainClassifier(GeneralDataset data) {
+ labelIndex = data.labelIndex();
+ featureIndex = data.featureIndex();
+ double[][] weights = trainWeights(data);
+ return new LinearClassifier(weights, featureIndex, labelIndex);
+ }
+
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/AdaptedGaussianPriorObjectiveFunction.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/AdaptedGaussianPriorObjectiveFunction.java
new file mode 100644
index 0000000..16521e1
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/AdaptedGaussianPriorObjectiveFunction.java
@@ -0,0 +1,118 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.math.ArrayMath;
+import java.util.Arrays;
+
+
+/**
+ * Adapt the mean of the Gaussian Prior by shifting the mean to the previously trained weights
+ * @author Pi-Chuan Chang
+ * @author Sarah Spikes (sdspikes@cs.stanford.edu) (Templatization)
+ *
+ * @param The type of the labels in the Dataset (one can be passed in to the constructor)
+ * @param The type of the features in the Dataset
+ */
+
+public class AdaptedGaussianPriorObjectiveFunction extends LogConditionalObjectiveFunction {
+
+ double[] weights;
+
+ /**
+ * Calculate the conditional likelihood.
+ */
+ @Override
+ protected void calculate(double[] x) {
+ if (useSummedConditionalLikelihood) {
+ calculateSCL(x);
+ } else {
+ calculateCL(x);
+ }
+ }
+
+
+ /**
+ */
+ private void calculateSCL(double[] x) {
+ throw new UnsupportedOperationException();
+ }
+
+ /**
+ */
+ private void calculateCL(double[] x) {
+ value = 0.0;
+ if (derivativeNumerator == null) {
+ derivativeNumerator = new double[x.length];
+ for (int d = 0; d < data.length; d++) {
+ int[] features = data[d];
+ for (int f = 0; f < features.length; f++) {
+ int i = indexOf(features[f], labels[d]);
+ if (dataweights == null) {
+ derivativeNumerator[i] -= 1;
+ } else {
+ derivativeNumerator[i] -= dataweights[d];
+ }
+ }
+ }
+ }
+ copy(derivative, derivativeNumerator);
+
+ double[] sums = new double[numClasses];
+ double[] probs = new double[numClasses];
+
+ for (int d = 0; d < data.length; d++) {
+ int[] features = data[d];
+ // activation
+ Arrays.fill(sums, 0.0);
+
+ for (int c = 0; c < numClasses; c++) {
+ for (int f = 0; f < features.length; f++) {
+ int i = indexOf(features[f], c);
+ sums[c] += x[i];
+ }
+ }
+ double total = ArrayMath.logSum(sums);
+ for (int c = 0; c < numClasses; c++) {
+ probs[c] = Math.exp(sums[c] - total);
+ if (dataweights != null) {
+ probs[c] *= dataweights[d];
+ }
+ for (int f = 0; f < features.length; f++) {
+ int i = indexOf(features[f], c);
+ derivative[i] += probs[c];
+ }
+ }
+
+ double dV = sums[labels[d]] - total;
+ if (dataweights != null) {
+ dV *= dataweights[d];
+ }
+ value -= dV;
+ }
+ //System.err.println("x length="+x.length);
+ //System.err.println("weights length="+weights.length);
+ double[] newX = ArrayMath.pairwiseSubtract(x, weights);
+ value += prior.compute(newX, derivative);
+ }
+
+ /**
+ */
+ @Override
+ protected void rvfcalculate(double[] x) {
+ throw new UnsupportedOperationException();
+ }
+
+ public AdaptedGaussianPriorObjectiveFunction(GeneralDataset dataset, LogPrior prior, double weights[][]) {
+ super(dataset, prior);
+ this.weights = to1D(weights);
+ }
+
+ public double[] to1D(double[][] x2) {
+ double[] x = new double[numFeatures*numClasses];
+ for (int i = 0; i < numFeatures; i++) {
+ for (int j = 0; j < numClasses; j++) {
+ x[indexOf(i, j)] = x2[i][j];
+ }
+ }
+ return x;
+ }
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/BiasedLogConditionalObjectiveFunction.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/BiasedLogConditionalObjectiveFunction.java
new file mode 100644
index 0000000..5378d14
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/BiasedLogConditionalObjectiveFunction.java
@@ -0,0 +1,137 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.math.ArrayMath;
+import edu.stanford.nlp.optimization.AbstractCachingDiffFunction;
+
+import java.util.Arrays;
+
+
+/**
+ * Maximizes the conditional likelihood with a given prior.
+ *
+ * @author Jenny Finkel
+ */
+
+public class BiasedLogConditionalObjectiveFunction extends AbstractCachingDiffFunction {
+
+ public void setPrior(LogPrior prior) {
+ this.prior = prior;
+ }
+
+ protected LogPrior prior;
+
+ protected int numFeatures = 0;
+ protected int numClasses = 0;
+
+ protected int[][] data = null;
+ protected int[] labels = null;
+
+ private double[][] confusionMatrix;
+
+ @Override
+ public int domainDimension() {
+ return numFeatures * numClasses;
+ }
+
+ int classOf(int index) {
+ return index % numClasses;
+ }
+
+ int featureOf(int index) {
+ return index / numClasses;
+ }
+
+ protected int indexOf(int f, int c) {
+ return f * numClasses + c;
+ }
+
+ public double[][] to2D(double[] x) {
+ double[][] x2 = new double[numFeatures][numClasses];
+ for (int i = 0; i < numFeatures; i++) {
+ for (int j = 0; j < numClasses; j++) {
+ x2[i][j] = x[indexOf(i, j)];
+ }
+ }
+ return x2;
+ }
+
+ @Override
+ protected void calculate(double[] x) {
+
+ if (derivative == null) {
+ derivative = new double[x.length];
+ } else {
+ Arrays.fill(derivative, 0.0);
+ }
+
+ value = 0.0;
+
+ double[] sums = new double[numClasses];
+ double[] probs = new double[numClasses];
+ double[] weightedProbs = new double[numClasses];
+
+ for (int d = 0; d < data.length; d++) {
+ int[] features = data[d];
+ int observedLabel = labels[d];
+ // activation
+ Arrays.fill(sums, 0.0);
+
+ for (int c = 0; c < numClasses; c++) {
+ for (int f = 0; f < features.length; f++) {
+ int i = indexOf(features[f], c);
+ sums[c] += x[i];
+ }
+ }
+
+ double total = ArrayMath.logSum(sums);
+
+ double[] weightedSums = new double[numClasses];
+ for (int trueLabel = 0; trueLabel < numClasses; trueLabel++) {
+ weightedSums[trueLabel] = Math.log(confusionMatrix[observedLabel][trueLabel]) + sums[trueLabel];
+ }
+
+ double weightedTotal = ArrayMath.logSum(weightedSums);
+
+ for (int c = 0; c < numClasses; c++) {
+ probs[c] = Math.exp(sums[c] - total);
+ weightedProbs[c] = Math.exp(weightedSums[c] - weightedTotal);
+ for (int f = 0; f < features.length; f++) {
+ int i = indexOf(features[f], c);
+ derivative[i] += probs[c] - weightedProbs[c];
+ }
+ }
+
+ double tmpValue = 0.0;
+ for (int c = 0; c < numClasses; c++) {
+ tmpValue += confusionMatrix[observedLabel][c] * Math.exp(sums[c] - total);
+ }
+ value -= Math.log(tmpValue);
+ }
+
+ value += prior.compute(x, derivative);
+
+ }
+
+
+
+ public BiasedLogConditionalObjectiveFunction(GeneralDataset dataset, double[][] confusionMatrix) {
+ this(dataset, confusionMatrix, new LogPrior(LogPrior.LogPriorType.QUADRATIC));
+ }
+
+ public BiasedLogConditionalObjectiveFunction(GeneralDataset dataset, double[][] confusionMatrix, LogPrior prior) {
+ this(dataset.numFeatures(), dataset.numClasses(), dataset.getDataArray(), dataset.getLabelsArray(), confusionMatrix, prior);
+ }
+
+ public BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix) {
+ this(numFeatures, numClasses, data, labels, confusionMatrix, new LogPrior(LogPrior.LogPriorType.QUADRATIC));
+ }
+
+ public BiasedLogConditionalObjectiveFunction(int numFeatures, int numClasses, int[][] data, int[] labels, double[][] confusionMatrix, LogPrior prior) {
+ this.numFeatures = numFeatures;
+ this.numClasses = numClasses;
+ this.data = data;
+ this.labels = labels;
+ this.prior = prior;
+ this.confusionMatrix = confusionMatrix;
+ }
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/BiasedLogisticObjectiveFunction.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/BiasedLogisticObjectiveFunction.java
new file mode 100644
index 0000000..6ef7fc5
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/BiasedLogisticObjectiveFunction.java
@@ -0,0 +1,147 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.optimization.AbstractCachingDiffFunction;
+
+import java.util.Arrays;
+
+
+/**
+ * @author jrfinkel
+ */
+public class BiasedLogisticObjectiveFunction extends AbstractCachingDiffFunction {
+
+ private final int numFeatures;
+ private final int[][] data;
+ private final double[][] dataValues;
+ private final int[] labels;
+ protected float[] dataweights = null;
+ private final LogPrior prior;
+ double probCorrect = 0.7;
+
+ @Override
+ public int domainDimension() {
+ return numFeatures;
+ }
+
+ @Override
+ protected void calculate(double[] x) {
+
+ if (dataValues != null) {
+ throw new RuntimeException();
+ }
+
+ value = 0.0;
+ Arrays.fill(derivative, 0.0);
+
+ for (int d = 0; d < data.length; d++) {
+ int[] features = data[d];
+ double sum = 0;
+
+ for (int f = 0; f < features.length; f++) {
+ sum += x[features[f]];
+ }
+
+ double expSum, derivativeIncrement;
+
+ if (dataweights != null) {
+ throw new RuntimeException();
+ }
+
+ if (labels[d] == 1) {
+ expSum = Math.exp(-sum);
+ double g = (1 / (1 + expSum));
+ value -= Math.log(g);
+ derivativeIncrement = (g-1);
+ } else {
+// expSum = Math.exp(-sum);
+// double g = (1 / (1 + expSum));
+// value -= Math.log(1-g);
+// derivativeIncrement = (g);
+// }
+ expSum = Math.exp(-sum);
+ double g = (1 / (1 + expSum));
+ double e = (1-probCorrect) * g + (probCorrect)*(1 - g);
+ value -= Math.log(e);
+ derivativeIncrement = -(g*(1-g)*(1-2*probCorrect)) / (e);
+ }
+
+ for (int f = 0; f < features.length; f++) {
+ derivative[features[f]] += derivativeIncrement;
+ }
+ }
+
+ value += prior.compute(x, derivative);
+ }
+
+ protected void calculateRVF(double[] x) {
+
+ value = 0.0;
+ Arrays.fill(derivative, 0.0);
+
+ for (int d = 0; d < data.length; d++) {
+ int[] features = data[d];
+ double[] values = dataValues[d];
+ double sum = 0;
+
+ for (int f = 0; f < features.length; f++) {
+ sum += x[features[f]]*values[features[f]];
+ }
+
+ double expSum, derivativeIncrement;
+
+ if (labels[d] == 0) {
+ expSum = Math.exp(sum);
+ derivativeIncrement = 1.0 / (1.0 + (1.0 / expSum));
+ } else {
+ expSum = Math.exp(-sum);
+ derivativeIncrement = -1.0 / (1.0 + (1.0 / expSum));
+ }
+
+ if (dataweights == null) {
+ value += Math.log(1.0 + expSum);
+ } else {
+ value += Math.log(1.0 + expSum) * dataweights[d];
+ derivativeIncrement *= dataweights[d];
+ }
+
+ for (int f = 0; f < features.length; f++) {
+ derivative[features[f]] += values[features[f]]*derivativeIncrement;
+ }
+ }
+
+ value += prior.compute(x, derivative);
+ }
+
+
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, int[] labels) {
+ this(numFeatures, data, labels, new LogPrior(LogPrior.LogPriorType.QUADRATIC));
+ }
+
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, int[] labels, LogPrior prior) {
+ this(numFeatures, data, labels, prior, null);
+ }
+
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, int[] labels, float[] dataweights) {
+ this(numFeatures, data, labels, new LogPrior(LogPrior.LogPriorType.QUADRATIC), dataweights);
+ }
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, int[] labels, LogPrior prior, float[] dataweights) {
+ this(numFeatures, data, null, labels, prior, dataweights);
+ }
+
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, double[][] values, int[] labels) {
+ this(numFeatures, data, values, labels, new LogPrior(LogPrior.LogPriorType.QUADRATIC));
+ }
+
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, double[][] values, int[] labels, LogPrior prior) {
+ this(numFeatures, data, values, labels, prior, null);
+ }
+
+ public BiasedLogisticObjectiveFunction(int numFeatures, int[][] data, double[][] values, int[] labels, LogPrior prior, float[] dataweights) {
+ this.numFeatures = numFeatures;
+ this.data = data;
+ this.labels = labels;
+ this.prior = prior;
+ this.dataweights = dataweights;
+ this.dataValues = values;
+ }
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/Classifier.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/Classifier.java
new file mode 100644
index 0000000..4998323
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/Classifier.java
@@ -0,0 +1,28 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.ling.Datum;
+import edu.stanford.nlp.stats.Counter;
+
+import java.io.Serializable;
+import java.util.Collection;
+
+/**
+ * A simple interface for classifying and scoring data points, implemented
+ * by most of the classifiers in this package. A basic Classifier
+ * works over a List of categorical features. For classifiers over
+ * real-valued features, see {@link RVFClassifier}.
+ *
+ * @author Dan Klein
+ * @author Sarah Spikes (sdspikes@cs.stanford.edu) (Templatization)
+ *
+ * @param The type of the label(s) in each Datum
+ * @param The type of the features in each Datum
+ */
+
+public interface Classifier extends Serializable {
+ public L classOf(Datum example);
+
+ public Counter scoresOf(Datum example);
+
+ public Collection labels();
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/ClassifierCreator.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/ClassifierCreator.java
new file mode 100644
index 0000000..d58905b
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/ClassifierCreator.java
@@ -0,0 +1,10 @@
+package edu.stanford.nlp.classify;
+
+/**
+ * Creates a classifier with given weights
+ *
+ * @author Angel Chang
+ */
+public interface ClassifierCreator {
+ public Classifier createClassifier(double[] weights);
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/ClassifierFactory.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/ClassifierFactory.java
new file mode 100644
index 0000000..4d4cb38
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/ClassifierFactory.java
@@ -0,0 +1,24 @@
+package edu.stanford.nlp.classify;
+
+import java.io.Serializable;
+import java.util.List;
+
+import edu.stanford.nlp.ling.RVFDatum;
+
+/**
+ * A simple interface for training a Classifier from a Dataset of training
+ * examples.
+ *
+ * @author Dan Klein
+ *
+ * Templatized by Sarah Spikes (sdspikes@cs.stanford.edu)
+ */
+
+public interface ClassifierFactory> extends Serializable {
+
+ @Deprecated //ClassifierFactory should implement trainClassifier(GeneralDataset) instead.
+ public C trainClassifier(List> examples);
+
+ public C trainClassifier(GeneralDataset dataset);
+
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/CrossValidator.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/CrossValidator.java
new file mode 100644
index 0000000..110d98a
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/CrossValidator.java
@@ -0,0 +1,94 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.util.Pair;
+import edu.stanford.nlp.util.Triple;
+import edu.stanford.nlp.util.Function;
+
+import java.util.Iterator;
+
+/**
+ * This class is meant to simplify performing cross validation of
+ * classifiers for hyper-parameters. It has the ability to save
+ * state for each fold (for instance, the weights for a MaxEnt
+ * classifier, and the alphas for an SVM).
+ *
+ * @author Aria Haghighi
+ * @author Jenny Finkel
+ * @author Sarah Spikes (Templatization)
+ */
+public class CrossValidator {
+ private final GeneralDataset originalTrainData;
+ private final int kFold;
+ private final SavedState[] savedStates;
+
+ public CrossValidator(GeneralDataset trainData) {
+ this(trainData, 10);
+ }
+
+ public CrossValidator(GeneralDataset trainData, int kFold) {
+ originalTrainData = trainData;
+ this.kFold = kFold;
+ savedStates = new SavedState[kFold];
+ for (int i = 0; i < savedStates.length; i++) {
+ savedStates[i] = new SavedState();
+ }
+ }
+
+ /**
+ * Returns an Iterator over train/test/saved states.
+ *
+ * @return An Iterator over train/test/saved states
+ */
+ private Iterator,GeneralDataset,SavedState>> iterator() { return new CrossValidationIterator(); }
+
+ /**
+ * This computes the average over all folds of the function we're trying to optimize.
+ * The input triple contains, in order, the train set, the test set, and the saved state.
+ * You don't have to use the saved state if you don't want to.
+ */
+ public double computeAverage (Function,GeneralDataset,SavedState>,Double> function)
+ {
+ double sum = 0;
+ Iterator,GeneralDataset,SavedState>> foldIt = iterator();
+ while (foldIt.hasNext()) {
+ sum += function.apply(foldIt.next());
+ }
+ return sum / kFold;
+ }
+
+ class CrossValidationIterator implements Iterator,GeneralDataset,SavedState>>
+ {
+ int iter = 0;
+ public boolean hasNext() { return iter < kFold; }
+
+ public void remove()
+ {
+ throw new RuntimeException("CrossValidationIterator doesn't support remove()");
+ }
+
+ public Triple,GeneralDataset,SavedState> next()
+ {
+ if (iter == kFold) return null;
+ int start = originalTrainData.size() * iter / kFold;
+ int end = originalTrainData.size() * (iter + 1) / kFold;
+ //System.err.println("##train data size: " + originalTrainData.size() + " start " + start + " end " + end);
+ Pair, GeneralDataset> split = originalTrainData.split(start, end);
+
+ return new Triple,GeneralDataset,SavedState>(split.first(),split.second(),savedStates[iter++]);
+ }
+ }
+
+ public static class SavedState {
+ public Object state;
+ }
+
+ public static void main(String[] args) {
+ Dataset d = Dataset.readSVMLightFormat(args[0]);
+ Iterator,GeneralDataset,SavedState>> it = (new CrossValidator(d)).iterator();
+ while (it.hasNext())
+ {
+ it.next();
+ break;
+ }
+ }
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/Dataset.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/Dataset.java
new file mode 100644
index 0000000..3786a54
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/Dataset.java
@@ -0,0 +1,758 @@
+package edu.stanford.nlp.classify;
+
+import java.io.PrintWriter;
+import java.io.StringWriter;
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Random;
+import java.util.Set;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+import edu.stanford.nlp.ling.BasicDatum;
+import edu.stanford.nlp.ling.Datum;
+import edu.stanford.nlp.ling.RVFDatum;
+import edu.stanford.nlp.stats.ClassicCounter;
+import edu.stanford.nlp.stats.Counter;
+import edu.stanford.nlp.stats.TwoDimensionalCounter;
+import edu.stanford.nlp.objectbank.ObjectBank;
+import edu.stanford.nlp.util.Generics;
+import edu.stanford.nlp.util.Index;
+import edu.stanford.nlp.util.HashIndex;
+import edu.stanford.nlp.util.Pair;
+import edu.stanford.nlp.util.ScoredComparator;
+import edu.stanford.nlp.util.ScoredObject;
+
+
+/**
+ * An interfacing class for {@link ClassifierFactory} that incrementally
+ * builds a more memory-efficient representation of a {@link List} of
+ * {@link Datum} objects for the purposes of training a {@link Classifier}
+ * with a {@link ClassifierFactory}.
+ *
+ * @author Roger Levy (rog@stanford.edu)
+ * @author Anna Rafferty (various refactoring with GeneralDataset/RVFDataset)
+ * @author Sarah Spikes (sdspikes@cs.stanford.edu) (templatization)
+ * @author nmramesh@cs.stanford.edu {@link #getL1NormalizedTFIDFDatum(Datum, Counter) and #getL1NormalizedTFIDFDataset()}
+ *
+ * @param Label type
+ * @param Feature type
+ */
+public class Dataset extends GeneralDataset {
+
+ private static final long serialVersionUID = -3883164942879961091L;
+
+ public Dataset() {
+ this(10);
+ }
+
+ public Dataset(int numDatums) {
+ initialize(numDatums);
+ }
+
+ public Dataset(int numDatums, Index featureIndex, Index labelIndex) {
+ initialize(numDatums);
+ this.featureIndex = featureIndex;
+ this.labelIndex = labelIndex;
+ }
+
+ public Dataset(Index featureIndex, Index labelIndex) {
+ this(10, featureIndex, labelIndex);
+ }
+
+
+ /**
+ * Constructor that fully specifies a Dataset. Needed this for MulticlassDataset.
+ */
+ public Dataset(Index labelIndex, int[] labels, Index featureIndex, int[][] data) {
+ this (labelIndex, labels, featureIndex, data, data.length);
+ }
+
+ /**
+ * Constructor that fully specifies a Dataset. Needed this for MulticlassDataset.
+ */
+ public Dataset(Index labelIndex, int[] labels, Index featureIndex, int[][] data, int size) {
+ this.labelIndex = labelIndex;
+ this.labels = labels;
+ this.featureIndex = featureIndex;
+ this.data = data;
+ this.size = size;
+ }
+
+ @Override
+ public Pair, GeneralDataset> split(double percentDev) {
+ int devSize = (int)(percentDev * size());
+ int trainSize = size() - devSize;
+
+ int[][] devData = new int[devSize][];
+ int[] devLabels = new int[devSize];
+
+ int[][] trainData = new int[trainSize][];
+ int[] trainLabels = new int[trainSize];
+
+ System.arraycopy(data, 0, devData, 0, devSize);
+ System.arraycopy(labels, 0, devLabels, 0, devSize);
+
+ System.arraycopy(data, devSize, trainData, 0, trainSize);
+ System.arraycopy(labels, devSize, trainLabels, 0, trainSize);
+
+ if (this instanceof WeightedDataset) {
+ float[] trainWeights = new float[trainSize];
+ float[] devWeights = new float[devSize];
+
+ WeightedDataset w = (WeightedDataset)this;
+
+ System.arraycopy(w.weights, 0, devWeights, 0, devSize);
+ System.arraycopy(w.weights, devSize, trainWeights, 0, trainSize);
+
+ WeightedDataset dev = new WeightedDataset(labelIndex, devLabels, featureIndex, devData, devSize, devWeights);
+ WeightedDataset train = new WeightedDataset(labelIndex, trainLabels, featureIndex, trainData, trainSize, trainWeights);
+
+ return new Pair,GeneralDataset>(train, dev);
+ }
+ Dataset dev = new Dataset(labelIndex, devLabels, featureIndex, devData, devSize);
+ Dataset train = new Dataset(labelIndex, trainLabels, featureIndex, trainData, trainSize);
+
+ return new Pair,GeneralDataset>(train, dev);
+ }
+
+ @Override
+ public Pair,GeneralDataset> split(int start, int end) {
+ int devSize = end - start;
+ int trainSize = size() - devSize;
+
+ int[][] devData = new int[devSize][];
+ int[] devLabels = new int[devSize];
+
+ int[][] trainData = new int[trainSize][];
+ int[] trainLabels = new int[trainSize];
+
+ System.arraycopy(data, start, devData, 0, devSize);
+ System.arraycopy(labels, start, devLabels, 0, devSize);
+
+ System.arraycopy(data, 0, trainData, 0, start);
+ System.arraycopy(data, end, trainData, start, size()-end);
+ System.arraycopy(labels, 0, trainLabels, 0, start);
+ System.arraycopy(labels, end, trainLabels, start, size()-end);
+
+ if (this instanceof WeightedDataset) {
+ float[] trainWeights = new float[trainSize];
+ float[] devWeights = new float[devSize];
+
+ WeightedDataset w = (WeightedDataset)this;
+
+ System.arraycopy(w.weights, start, devWeights, 0, devSize);
+ System.arraycopy(w.weights, 0, trainWeights, 0, start);
+ System.arraycopy(w.weights, end, trainWeights, start, size()-end);
+
+ WeightedDataset dev = new WeightedDataset(labelIndex, devLabels, featureIndex, devData, devSize, devWeights);
+ WeightedDataset train = new WeightedDataset(labelIndex, trainLabels, featureIndex, trainData, trainSize, trainWeights);
+
+ return new Pair,GeneralDataset>(train, dev);
+ }
+ Dataset dev = new Dataset(labelIndex, devLabels, featureIndex, devData, devSize);
+ Dataset train = new Dataset(labelIndex, trainLabels, featureIndex, trainData, trainSize);
+
+ return new Pair,GeneralDataset>(train, dev);
+ }
+
+
+ public Dataset getRandomSubDataset(double p, int seed) {
+ int newSize = (int)(p * size());
+ Set indicesToKeep = Generics.newHashSet();
+ Random r = new Random(seed);
+ int s = size();
+ while (indicesToKeep.size() < newSize) {
+ indicesToKeep.add(r.nextInt(s));
+ }
+
+ int[][] newData = new int[newSize][];
+ int[] newLabels = new int[newSize];
+
+ int i = 0;
+ for (int j : indicesToKeep) {
+ newData[i] = data[j];
+ newLabels[i] = labels[j];
+ i++;
+ }
+
+ return new Dataset(labelIndex, newLabels, featureIndex, newData);
+ }
+
+ @Override
+ public double[][] getValuesArray() {
+ return null;
+ }
+
+ /**
+ * Constructs a Dataset by reading in a file in SVM light format.
+ */
+ public static Dataset readSVMLightFormat(String filename) {
+ return readSVMLightFormat(filename, new HashIndex(), new HashIndex());
+ }
+
+ /**
+ * Constructs a Dataset by reading in a file in SVM light format.
+ * The lines parameter is filled with the lines of the file for further processing
+ * (if lines is null, it is assumed no line information is desired)
+ */
+ public static Dataset readSVMLightFormat(String filename, List lines) {
+ return readSVMLightFormat(filename, new HashIndex(), new HashIndex(), lines);
+ }
+
+ /**
+ * Constructs a Dataset by reading in a file in SVM light format.
+ * the created dataset has the same feature and label index as given
+ */
+ public static Dataset readSVMLightFormat(String filename, Index featureIndex, Index labelIndex) {
+ return readSVMLightFormat(filename, featureIndex, labelIndex, null);
+ }
+ /**
+ * Constructs a Dataset by reading in a file in SVM light format.
+ * the created dataset has the same feature and label index as given
+ */
+ public static Dataset readSVMLightFormat(String filename, Index featureIndex, Index labelIndex, List lines) {
+ Dataset dataset;
+ try {
+ dataset = new Dataset(10, featureIndex, labelIndex);
+ for (String line : ObjectBank.getLineIterator(new File(filename))) {
+ if(lines != null)
+ lines.add(line);
+ dataset.add(svmLightLineToDatum(line));
+ }
+
+ } catch (Exception e) {
+ throw new RuntimeException(e);
+ }
+ return dataset;
+ }
+
+ private static int line1 = 0;
+
+ public static Datum svmLightLineToDatum(String l) {
+ line1++;
+ l = l.replaceAll("#.*", ""); // remove any trailing comments
+ String[] line = l.split("\\s+");
+ Collection features = new ArrayList();
+ for (int i = 1; i < line.length; i++) {
+ String[] f = line[i].split(":");
+ if (f.length != 2) {
+ System.err.println("Dataset error: line " + line1);
+ }
+ int val = (int) Double.parseDouble(f[1]);
+ for (int j = 0; j < val; j++) {
+ features.add(f[0]);
+ }
+ }
+ features.add(String.valueOf(Integer.MAX_VALUE)); // a constant feature for a class
+ Datum d = new BasicDatum(features, line[0]);
+ return d;
+ }
+
+ /**
+ * Get Number of datums a given feature appears in.
+ */
+ public Counter getFeatureCounter()
+ {
+ Counter featureCounts = new ClassicCounter();
+ for (int i=0; i < this.size(); i++)
+ {
+ BasicDatum datum = (BasicDatum) getDatum(i);
+ Set featureSet = Generics.newHashSet(datum.asFeatures());
+ for (F key : featureSet) {
+ featureCounts.incrementCount(key, 1.0);
+ }
+ }
+ return featureCounts;
+ }
+
+ /**
+ * Method to convert features from counts to L1-normalized TFIDF based features
+ * @param datum with a collection of features.
+ * @param featureDocCounts a counter of doc-count for each feature.
+ * @return RVFDatum with l1-normalized tf-idf features.
+ */
+ public RVFDatum getL1NormalizedTFIDFDatum(Datum datum,Counter featureDocCounts){
+ Counter tfidfFeatures = new ClassicCounter();
+ for(F feature : datum.asFeatures()){
+ if(featureDocCounts.containsKey(feature))
+ tfidfFeatures.incrementCount(feature,1.0);
+ }
+ double l1norm = 0;
+ for(F feature: tfidfFeatures.keySet()){
+ double idf = Math.log(((double)(this.size()+1))/(featureDocCounts.getCount(feature)+0.5));
+ double tf = tfidfFeatures.getCount(feature);
+ tfidfFeatures.setCount(feature, tf*idf);
+ l1norm += tf*idf;
+ }
+ for(F feature: tfidfFeatures.keySet()){
+ double tfidf = tfidfFeatures.getCount(feature);
+ tfidfFeatures.setCount(feature, tfidf/l1norm);
+ }
+ RVFDatum rvfDatum = new RVFDatum(tfidfFeatures,datum.label());
+ return rvfDatum;
+ }
+
+ /**
+ * Method to convert this dataset to RVFDataset using L1-normalized TF-IDF features
+ * @return RVFDataset
+ */
+ public RVFDataset getL1NormalizedTFIDFDataset(){
+ RVFDataset rvfDataset = new RVFDataset(this.size(),this.featureIndex,this.labelIndex);
+ Counter featureDocCounts = getFeatureCounter();
+ for(int i = 0; i < this.size(); i++){
+ Datum datum = this.getDatum(i);
+ RVFDatum rvfDatum = getL1NormalizedTFIDFDatum(datum,featureDocCounts);
+ rvfDataset.add(rvfDatum);
+ }
+ return rvfDataset;
+ }
+
+ @Override
+ public void add(Datum d) {
+ add(d.asFeatures(), d.label());
+ }
+
+ public void add(Collection features, L label) {
+ add(features, label, true);
+ }
+
+ public void add(Collection features, L label, boolean addNewFeatures) {
+ ensureSize();
+ addLabel(label);
+ addFeatures(features, addNewFeatures);
+ size++;
+ }
+
+ /**
+ * Adds a datums defined by feature indices and label index
+ * Careful with this one! Make sure that all indices are valid!
+ * @param features
+ * @param label
+ */
+ public void add(int [] features, int label) {
+ ensureSize();
+ addLabelIndex(label);
+ addFeatureIndices(features);
+ size++;
+ }
+
+ protected void ensureSize() {
+ if (labels.length == size) {
+ int[] newLabels = new int[size * 2];
+ System.arraycopy(labels, 0, newLabels, 0, size);
+ labels = newLabels;
+ int[][] newData = new int[size * 2][];
+ System.arraycopy(data, 0, newData, 0, size);
+ data = newData;
+ }
+ }
+
+ protected void addLabel(L label) {
+ labelIndex.add(label);
+ labels[size] = labelIndex.indexOf(label);
+ }
+
+ protected void addLabelIndex(int label) {
+ labels[size] = label;
+ }
+
+ protected void addFeatures(Collection features) {
+ addFeatures(features, true);
+ }
+
+ protected void addFeatures(Collection features, boolean addNewFeatures) {
+ int[] intFeatures = new int[features.size()];
+ int j = 0;
+ for (F feature : features) {
+ if(addNewFeatures) featureIndex.add(feature);
+ int index = featureIndex.indexOf(feature);
+ if (index >= 0) {
+ intFeatures[j] = featureIndex.indexOf(feature);
+ j++;
+ }
+ }
+ data[size] = new int[j];
+ System.arraycopy(intFeatures, 0, data[size], 0, j);
+ }
+
+ protected void addFeatureIndices(int [] features) {
+ data[size] = features;
+ }
+
+ @Override
+ protected final void initialize(int numDatums) {
+ labelIndex = new HashIndex();
+ featureIndex = new HashIndex();
+ labels = new int[numDatums];
+ data = new int[numDatums][];
+ size = 0;
+ }
+
+ /**
+ * @return the index-ed datum
+ */
+ @Override
+ public Datum getDatum(int index) {
+ return new BasicDatum(featureIndex.objects(data[index]), labelIndex.get(labels[index]));
+ }
+
+ /**
+ * @return the index-ed datum
+ */
+ @Override
+ public RVFDatum getRVFDatum(int index) {
+ ClassicCounter c = new ClassicCounter();
+ for (F key : featureIndex.objects(data[index])) {
+ c.incrementCount(key);
+ }
+ return new RVFDatum(c, labelIndex.get(labels[index]));
+ }
+
+ /**
+ * Prints some summary statistics to stderr for the Dataset.
+ */
+ @Override
+ public void summaryStatistics() {
+ System.err.println(toSummaryStatistics());
+ }
+
+ public String toSummaryStatistics() {
+ StringBuilder sb = new StringBuilder();
+ sb.append("numDatums: ").append(size).append('\n');
+ sb.append("numLabels: ").append(labelIndex.size()).append(" [");
+ Iterator iter = labelIndex.iterator();
+ while (iter.hasNext()) {
+ sb.append(iter.next());
+ if (iter.hasNext()) {
+ sb.append(", ");
+ }
+ }
+ sb.append("]\n");
+ sb.append("numFeatures (Phi(X) types): ").append(featureIndex.size()).append('\n');
+ // List l = new ArrayList(featureIndex);
+// Collections.sort(l);
+// sb.append(l);
+ return sb.toString();
+ }
+
+
+ /**
+ * Applies feature count thresholds to the Dataset.
+ * Only features that match pattern_i and occur at
+ * least threshold_i times (for some i) are kept.
+ *
+ * @param thresholds a list of pattern, threshold pairs
+ */
+ public void applyFeatureCountThreshold(List> thresholds) {
+
+ // get feature counts
+ float[] counts = getFeatureCounts();
+
+ // build a new featureIndex
+ Index newFeatureIndex = new HashIndex();
+ LOOP:
+ for (F f : featureIndex) {
+ for (Pair threshold : thresholds) {
+ Pattern p = threshold.first();
+ Matcher m = p.matcher(f.toString());
+ if (m.matches()) {
+ if (counts[featureIndex.indexOf(f)] >= threshold.second) {
+ newFeatureIndex.add(f);
+ }
+ continue LOOP;
+ }
+ }
+ // we only get here if it didn't match anything on the list
+ newFeatureIndex.add(f);
+ }
+
+ counts = null;
+
+ int[] featMap = new int[featureIndex.size()];
+ for (int i = 0; i < featMap.length; i++) {
+ featMap[i] = newFeatureIndex.indexOf(featureIndex.get(i));
+ }
+
+ featureIndex = null;
+
+ for (int i = 0; i < size; i++) {
+ List featList = new ArrayList(data[i].length);
+ for (int j = 0; j < data[i].length; j++) {
+ if (featMap[data[i][j]] >= 0) {
+ featList.add(featMap[data[i][j]]);
+ }
+ }
+ data[i] = new int[featList.size()];
+ for (int j = 0; j < data[i].length; j++) {
+ data[i][j] = featList.get(j);
+ }
+ }
+
+ featureIndex = newFeatureIndex;
+ }
+
+
+ /**
+ * prints the full feature matrix in tab-delimited form. These can be BIG
+ * matrices, so be careful!
+ */
+ public void printFullFeatureMatrix(PrintWriter pw) {
+ String sep = "\t";
+ for (int i = 0; i < featureIndex.size(); i++) {
+ pw.print(sep + featureIndex.get(i));
+ }
+ pw.println();
+ for (int i = 0; i < labels.length; i++) {
+ pw.print(labelIndex.get(i));
+ Set feats = Generics.newHashSet();
+ for (int j = 0; j < data[i].length; j++) {
+ int feature = data[i][j];
+ feats.add(Integer.valueOf(feature));
+ }
+ for (int j = 0; j < featureIndex.size(); j++) {
+ if (feats.contains(Integer.valueOf(j))) {
+ pw.print(sep + '1');
+ } else {
+ pw.print(sep + '0');
+ }
+ }
+ }
+ }
+
+ /**
+ * prints the sparse feature matrix using {@link #printSparseFeatureMatrix()}
+ * to {@link System#out System.out}.
+ */
+ public void printSparseFeatureMatrix() {
+ printSparseFeatureMatrix(new PrintWriter(System.out, true));
+ }
+
+ /**
+ * prints a sparse feature matrix representation of the Dataset. Prints the actual
+ * {@link Object#toString()} representations of features.
+ */
+ public void printSparseFeatureMatrix(PrintWriter pw) {
+ String sep = "\t";
+ for (int i = 0; i < size; i++) {
+ pw.print(labelIndex.get(labels[i]));
+ int[] datum = data[i];
+ for (int j : datum) {
+ pw.print(sep + featureIndex.get(j));
+ }
+ pw.println();
+ }
+ }
+
+
+ public void changeLabelIndex(Index newLabelIndex) {
+
+ labels = trimToSize(labels);
+
+ for (int i = 0; i < labels.length; i++) {
+ labels[i] = newLabelIndex.indexOf(labelIndex.get(labels[i]));
+ }
+ labelIndex = newLabelIndex;
+ }
+
+ public void changeFeatureIndex(Index newFeatureIndex) {
+
+ data = trimToSize(data);
+ labels = trimToSize(labels);
+
+ int[][] newData = new int[data.length][];
+ for (int i = 0; i < data.length; i++) {
+ int[] newD = new int[data[i].length];
+ int k = 0;
+ for (int j = 0; j < data[i].length; j++) {
+ int newIndex = newFeatureIndex.indexOf(featureIndex.get(data[i][j]));
+ if (newIndex >= 0) {
+ newD[k++] = newIndex;
+ }
+ }
+ newData[i] = new int[k];
+ System.arraycopy(newD, 0, newData[i], 0, k);
+ }
+ data = newData;
+ featureIndex = newFeatureIndex;
+ }
+
+ public void selectFeaturesBinaryInformationGain(int numFeatures) {
+ double[] scores = getInformationGains();
+ selectFeatures(numFeatures,scores);
+ }
+
+ /**
+ * Generic method to select features based on the feature scores vector provided as an argument.
+ * @param numFeatures number of features to be selected.
+ * @param scores a vector of size total number of features in the data.
+ */
+ public void selectFeatures(int numFeatures, double[] scores) {
+
+ List> scoredFeatures = new ArrayList>();
+
+ for (int i = 0; i < scores.length; i++) {
+ scoredFeatures.add(new ScoredObject(featureIndex.get(i), scores[i]));
+ }
+
+ Collections.sort(scoredFeatures, ScoredComparator.DESCENDING_COMPARATOR);
+ Index newFeatureIndex = new HashIndex();
+ for (int i = 0; i < scoredFeatures.size() && i < numFeatures; i++) {
+ newFeatureIndex.add(scoredFeatures.get(i).object());
+ //System.err.println(scoredFeatures.get(i));
+ }
+
+ for (int i = 0; i < size; i++) {
+ int[] newData = new int[data[i].length];
+ int curIndex = 0;
+ for (int j = 0; j < data[i].length; j++) {
+ int index;
+ if ((index = newFeatureIndex.indexOf(featureIndex.get(data[i][j]))) != -1) {
+ newData[curIndex++] = index;
+ }
+ }
+ int[] newDataTrimmed = new int[curIndex];
+ System.arraycopy(newData, 0, newDataTrimmed, 0, curIndex);
+ data[i] = newDataTrimmed;
+ }
+ featureIndex = newFeatureIndex;
+ }
+
+
+ public double[] getInformationGains() {
+
+ data = trimToSize(data);
+ labels = trimToSize(labels);
+
+ // counts the number of times word X is present
+ ClassicCounter featureCounter = new ClassicCounter();
+
+ // counts the number of time a document has label Y
+ ClassicCounter labelCounter = new ClassicCounter();
+
+ // counts the number of times the document has label Y given word X is present
+ TwoDimensionalCounter condCounter = new TwoDimensionalCounter();
+
+ for (int i = 0; i < labels.length; i++) {
+ labelCounter.incrementCount(labelIndex.get(labels[i]));
+
+ // convert the document to binary feature representation
+ boolean[] doc = new boolean[featureIndex.size()];
+ //System.err.println(i);
+ for (int j = 0; j < data[i].length; j++) {
+ doc[data[i][j]] = true;
+ }
+
+ for (int j = 0; j < doc.length; j++) {
+ if (doc[j]) {
+ featureCounter.incrementCount(featureIndex.get(j));
+ condCounter.incrementCount(featureIndex.get(j), labelIndex.get(labels[i]), 1.0);
+ }
+ }
+ }
+
+ double entropy = 0.0;
+ for (int i = 0; i < labelIndex.size(); i++) {
+ double labelCount = labelCounter.getCount(labelIndex.get(i));
+ double p = labelCount / size();
+ entropy -= p * (Math.log(p) / Math.log(2));
+ }
+
+ double[] ig = new double[featureIndex.size()];
+ Arrays.fill(ig, entropy);
+
+ for (int i = 0; i < featureIndex.size(); i++) {
+ F feature = featureIndex.get(i);
+
+ double featureCount = featureCounter.getCount(feature);
+ double notFeatureCount = size() - featureCount;
+
+ double pFeature = featureCount / size();
+ double pNotFeature = (1.0 - pFeature);
+
+ if (featureCount == 0) { ig[i] = 0; continue; }
+ if (notFeatureCount == 0) { ig[i] = 0; continue; }
+
+ double sumFeature = 0.0;
+ double sumNotFeature = 0.0;
+
+ for (int j = 0; j < labelIndex.size(); j++) {
+ L label = labelIndex.get(j);
+
+ double featureLabelCount = condCounter.getCount(feature, label);
+ double notFeatureLabelCount = size() - featureLabelCount;
+
+ // yes, these dont sum to 1. that is correct.
+ // one is the prob of the label, given that the
+ // feature is present, and the other is the prob
+ // of the label given that the feature is absent
+ double p = featureLabelCount / featureCount;
+ double pNot = notFeatureLabelCount / notFeatureCount;
+
+ if (featureLabelCount != 0) {
+ sumFeature += p * (Math.log(p) / Math.log(2));
+ }
+
+ if (notFeatureLabelCount != 0) {
+ sumNotFeature += pNot * (Math.log(pNot) / Math.log(2));
+ }
+ //System.out.println(pNot+" "+(Math.log(pNot)/Math.log(2)));
+
+ }
+
+ //System.err.println(pFeature+" * "+sumFeature+" = +"+);
+ //System.err.println("^ "+pNotFeature+" "+sumNotFeature);
+
+ ig[i] += pFeature*sumFeature + pNotFeature*sumNotFeature;
+ /* earlier the line above used to be: ig[i] = pFeature*sumFeature + pNotFeature*sumNotFeature;
+ * This completely ignored the entropy term computed above. So added the "+=" to take that into account.
+ * -Ramesh (nmramesh@cs.stanford.edu)
+ */
+ }
+ return ig;
+ }
+
+ public void updateLabels(int[] labels) {
+ if (labels.length != size())
+ throw new IllegalArgumentException(
+ "size of labels array does not match dataset size");
+
+ this.labels = labels;
+ }
+
+ @Override
+ public String toString() {
+ return "Dataset of size " + size;
+ }
+
+ public String toSummaryString() {
+ StringWriter sw = new StringWriter();
+ PrintWriter pw = new PrintWriter(sw);
+ pw.println("Number of data points: " + size());
+ pw.println("Number of active feature tokens: " + numFeatureTokens());
+ pw.println("Number of active feature types:" + numFeatureTypes());
+ return pw.toString();
+ }
+
+ /**
+ * Need to sort the counter by feature keys and dump it
+ *
+ */
+ public static void printSVMLightFormat(PrintWriter pw, ClassicCounter c, int classNo) {
+ Integer[] features = c.keySet().toArray(new Integer[c.keySet().size()]);
+ Arrays.sort(features);
+ StringBuilder sb = new StringBuilder();
+ sb.append(classNo);
+ sb.append(' ');
+ for (int f: features) {
+ sb.append(f + 1).append(':').append(c.getCount(f)).append(' ');
+ }
+ pw.println(sb.toString());
+ }
+
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/GeneralDataset.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/GeneralDataset.java
new file mode 100644
index 0000000..1d368ce
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/GeneralDataset.java
@@ -0,0 +1,490 @@
+package edu.stanford.nlp.classify;
+
+import java.io.PrintWriter;
+import java.io.Serializable;
+import java.util.*;
+
+import edu.stanford.nlp.ling.BasicDatum;
+import edu.stanford.nlp.ling.Datum;
+import edu.stanford.nlp.ling.RVFDatum;
+import edu.stanford.nlp.stats.ClassicCounter;
+import edu.stanford.nlp.stats.Counter;
+import edu.stanford.nlp.util.Generics;
+import edu.stanford.nlp.util.HashIndex;
+import edu.stanford.nlp.util.Index;
+import edu.stanford.nlp.util.Pair;
+
+/**
+ * The purpose of this interface is to unify {@link Dataset} and {@link RVFDataset}.
+ *
+ * @author Kristina Toutanova (kristina@cs.stanford.edu)
+ * @author Anna Rafferty (various refactoring with subclasses)
+ * @author Sarah Spikes (sdspikes@cs.stanford.edu) (Templatization)
+ * @author Ramesh Nallapati (nmramesh@cs.stanford.edu)
+ * (added an abstract method getDatum, July 17th, 2008)
+ *
+ * @param The type of the labels in the Dataset
+ * @param The type of the features in the Dataset
+ */
+public abstract class GeneralDataset implements Serializable, Iterable> {
+
+ private static final long serialVersionUID = 19157757130054829L;
+
+ public Index labelIndex;
+ public Index featureIndex;
+
+ protected int[] labels;
+ protected int[][] data;
+
+ protected int size;
+
+ public GeneralDataset() { }
+
+ public Index labelIndex() { return labelIndex; }
+
+ public Index featureIndex() { return featureIndex; }
+
+ public int numFeatures() { return featureIndex.size(); }
+
+ public int numClasses() { return labelIndex.size(); }
+
+ public int[] getLabelsArray() {
+ labels = trimToSize(labels);
+ return labels;
+ }
+
+ public int[][] getDataArray() {
+ data = trimToSize(data);
+ return data;
+ }
+
+ public abstract double[][] getValuesArray();
+
+ /**
+ * Resets the Dataset so that it is empty and ready to collect data.
+ */
+ public void clear() {
+ clear(10);
+ }
+
+ /**
+ * Resets the Dataset so that it is empty and ready to collect data.
+ * @param numDatums initial capacity of dataset
+ */
+ public void clear(int numDatums) {
+ initialize(numDatums);
+ }
+
+ /**
+ * This method takes care of resetting values of the dataset
+ * such that it is empty with an initial capacity of numDatums.
+ * Should be accessed only by appropriate methods within the class,
+ * such as clear(), which take care of other parts of the emptying of data.
+ *
+ * @param numDatums initial capacity of dataset
+ */
+ protected abstract void initialize(int numDatums);
+
+
+ public abstract RVFDatum getRVFDatum(int index);
+
+ public abstract Datum getDatum(int index);
+
+
+ public abstract void add(Datum d);
+
+ /**
+ * Get the total count (over all data instances) of each feature
+ *
+ * @return an array containing the counts (indexed by index)
+ */
+ public float[] getFeatureCounts() {
+ float[] counts = new float[featureIndex.size()];
+ for (int i = 0, m = size; i < m; i++) {
+ for (int j = 0, n = data[i].length; j < n; j++) {
+ counts[data[i][j]] += 1.0;
+ }
+ }
+ return counts;
+ }
+
+ /**
+ * Applies a feature count threshold to the Dataset. All features that
+ * occur fewer than k times are expunged.
+ */
+ public void applyFeatureCountThreshold(int k) {
+ float[] counts = getFeatureCounts();
+ Index newFeatureIndex = new HashIndex();
+
+ int[] featMap = new int[featureIndex.size()];
+ for (int i = 0; i < featMap.length; i++) {
+ F feat = featureIndex.get(i);
+ if (counts[i] >= k) {
+ int newIndex = newFeatureIndex.size();
+ newFeatureIndex.add(feat);
+ featMap[i] = newIndex;
+ } else {
+ featMap[i] = -1;
+ }
+ // featureIndex.remove(feat);
+ }
+
+ featureIndex = newFeatureIndex;
+ // counts = null; // This is unnecessary; JVM can clean it up
+
+ for (int i = 0; i < size; i++) {
+ List featList = new ArrayList(data[i].length);
+ for (int j = 0; j < data[i].length; j++) {
+ if (featMap[data[i][j]] >= 0) {
+ featList.add(featMap[data[i][j]]);
+ }
+ }
+ data[i] = new int[featList.size()];
+ for (int j = 0; j < data[i].length; j++) {
+ data[i][j] = featList.get(j);
+ }
+ }
+ }
+
+
+ /**
+ * Applies a max feature count threshold to the Dataset. All features that
+ * occur greater than k times are expunged.
+ */
+ public void applyFeatureMaxCountThreshold(int k) {
+ float[] counts = getFeatureCounts();
+ HashIndex newFeatureIndex = new HashIndex();
+
+ int[] featMap = new int[featureIndex.size()];
+ for (int i = 0; i < featMap.length; i++) {
+ F feat = featureIndex.get(i);
+ if (counts[i] <= k) {
+ int newIndex = newFeatureIndex.size();
+ newFeatureIndex.add(feat);
+ featMap[i] = newIndex;
+ } else {
+ featMap[i] = -1;
+ }
+ // featureIndex.remove(feat);
+ }
+
+ featureIndex = newFeatureIndex;
+ // counts = null; // This is unnecessary; JVM can clean it up
+
+ for (int i = 0; i < size; i++) {
+ List featList = new ArrayList(data[i].length);
+ for (int j = 0; j < data[i].length; j++) {
+ if (featMap[data[i][j]] >= 0) {
+ featList.add(featMap[data[i][j]]);
+ }
+ }
+ data[i] = new int[featList.size()];
+ for (int j = 0; j < data[i].length; j++) {
+ data[i][j] = featList.get(j);
+ }
+ }
+ }
+
+
+ /**
+ * returns the number of feature tokens in the Dataset.
+ */
+ public int numFeatureTokens() {
+ int x = 0;
+ for (int i = 0, m = size; i < m; i++) {
+ x += data[i].length;
+ }
+ return x;
+ }
+
+ /**
+ * returns the number of distinct feature types in the Dataset.
+ */
+ public int numFeatureTypes() {
+ return featureIndex.size();
+ }
+
+
+
+ /**
+ * Adds all Datums in the given collection of data to this dataset
+ * @param data collection of datums you would like to add to the dataset
+ */
+ public void addAll(Iterable> data) {
+ for (Datum d : data) {
+ add(d);
+ }
+ }
+
+ public abstract Pair, GeneralDataset> split (int start, int end) ;
+ public abstract Pair, GeneralDataset> split (double p) ;
+
+ /**
+ * Returns the number of examples ({@link Datum}s) in the Dataset.
+ */
+ public int size() { return size; }
+
+ protected void trimData() {
+ data = trimToSize(data);
+ }
+
+ protected void trimLabels() {
+ labels = trimToSize(labels);
+ }
+
+ protected int[] trimToSize(int[] i) {
+ int[] newI = new int[size];
+ System.arraycopy(i, 0, newI, 0, size);
+ return newI;
+ }
+
+ protected int[][] trimToSize(int[][] i) {
+ int[][] newI = new int[size][];
+ System.arraycopy(i, 0, newI, 0, size);
+ return newI;
+ }
+
+ protected double[][] trimToSize(double[][] i) {
+ double[][] newI = new double[size][];
+ System.arraycopy(i, 0, newI, 0, size);
+ return newI;
+ }
+
+ /**
+ * Randomizes the data array in place.
+ * Note: this cannot change the values array or the datum weights,
+ * so redefine this for RVFDataset and WeightedDataset!
+ * @param randomSeed
+ */
+ public void randomize(int randomSeed) {
+ Random rand = new Random(randomSeed);
+ for(int j = size - 1; j > 0; j --){
+ int randIndex = rand.nextInt(j);
+
+ int [] tmp = data[randIndex];
+ data[randIndex] = data[j];
+ data[j] = tmp;
+
+ int tmpl = labels[randIndex];
+ labels[randIndex] = labels[j];
+ labels[j] = tmpl;
+ }
+ }
+
+ public GeneralDataset sampleDataset(int randomSeed, double sampleFrac, boolean sampleWithReplacement) {
+ int sampleSize = (int)(this.size()*sampleFrac);
+ Random rand = new Random(randomSeed);
+ GeneralDataset subset;
+ if(this instanceof RVFDataset)
+ subset = new RVFDataset();
+ else if (this instanceof Dataset) {
+ subset = new Dataset();
+ }
+ else {
+ throw new RuntimeException("Can't handle this type of GeneralDataset.");
+ }
+ if (sampleWithReplacement) {
+ for(int i = 0; i < sampleSize; i++){
+ int datumNum = rand.nextInt(this.size());
+ subset.add(this.getDatum(datumNum));
+ }
+ } else {
+ Set indicedSampled = Generics.newHashSet();
+ while (subset.size() < sampleSize) {
+ int datumNum = rand.nextInt(this.size());
+ if (!indicedSampled.contains(datumNum)) {
+ subset.add(this.getDatum(datumNum));
+ indicedSampled.add(datumNum);
+ }
+ }
+ }
+ return subset;
+ }
+
+ /**
+ * Print some statistics summarizing the dataset
+ *
+ */
+ public abstract void summaryStatistics();
+
+ /**
+ * Returns an iterator over the class labels of the Dataset
+ *
+ * @return An iterator over the class labels of the Dataset
+ */
+ public Iterator labelIterator() {
+ return labelIndex.iterator();
+ }
+
+
+ /**
+ *
+ * @param dataset
+ * @return a new GeneralDataset whose features and ids map exactly to those of this GeneralDataset.
+ * Useful when two Datasets are created independently and one wants to train a model on one dataset and test on the other. -Ramesh.
+ */
+ public GeneralDataset mapDataset(GeneralDataset dataset){
+ GeneralDataset newDataset;
+ if(dataset instanceof RVFDataset)
+ newDataset = new RVFDataset(this.featureIndex,this.labelIndex);
+ else newDataset = new Dataset(this.featureIndex,this.labelIndex);
+ this.featureIndex.lock();
+ this.labelIndex.lock();
+ //System.out.println("inside mapDataset: dataset size:"+dataset.size());
+ for(int i = 0; i < dataset.size(); i++)
+ //System.out.println("inside mapDataset: adding datum number"+i);
+ newDataset.add(dataset.getDatum(i));
+
+ //System.out.println("old Dataset stats: numData:"+dataset.size()+" numfeatures:"+dataset.featureIndex().size()+" numlabels:"+dataset.labelIndex.size());
+ //System.out.println("new Dataset stats: numData:"+newDataset.size()+" numfeatures:"+newDataset.featureIndex().size()+" numlabels:"+newDataset.labelIndex.size());
+ //System.out.println("this dataset stats: numData:"+size()+" numfeatures:"+featureIndex().size()+" numlabels:"+labelIndex.size());
+
+ this.featureIndex.unlock();
+ this.labelIndex.unlock();
+ return newDataset;
+ }
+
+ public static Datum mapDatum(Datum d, Map labelMapping, L2 defaultLabel) {
+ // TODO: How to copy datum?
+ L2 newLabel = labelMapping.get(d.label());
+ if (newLabel == null) {
+ newLabel = defaultLabel;
+ }
+
+ if (d instanceof RVFDatum) {
+ return new RVFDatum( ((RVFDatum) d).asFeaturesCounter(), newLabel );
+ } else {
+ return new BasicDatum( d.asFeatures(), newLabel );
+ }
+ }
+
+
+ /**
+ *
+ * @param dataset
+ * @return a new GeneralDataset whose features and ids map exactly to those of this GeneralDataset. But labels are converted to be another set of labels
+ */
+ public GeneralDataset mapDataset(GeneralDataset dataset, Index newLabelIndex, Map labelMapping, L2 defaultLabel)
+ {
+ GeneralDataset newDataset;
+ if(dataset instanceof RVFDataset)
+ newDataset = new RVFDataset(this.featureIndex, newLabelIndex);
+ else newDataset = new Dataset(this.featureIndex, newLabelIndex);
+ this.featureIndex.lock();
+ this.labelIndex.lock();
+ //System.out.println("inside mapDataset: dataset size:"+dataset.size());
+ for(int i = 0; i < dataset.size(); i++) {
+ //System.out.println("inside mapDataset: adding datum number"+i);
+ Datum d = dataset.getDatum(i);
+ Datum d2 = mapDatum(d, labelMapping, defaultLabel);
+ newDataset.add(d2);
+ }
+ //System.out.println("old Dataset stats: numData:"+dataset.size()+" numfeatures:"+dataset.featureIndex().size()+" numlabels:"+dataset.labelIndex.size());
+ //System.out.println("new Dataset stats: numData:"+newDataset.size()+" numfeatures:"+newDataset.featureIndex().size()+" numlabels:"+newDataset.labelIndex.size());
+ //System.out.println("this dataset stats: numData:"+size()+" numfeatures:"+featureIndex().size()+" numlabels:"+labelIndex.size());
+
+ this.featureIndex.unlock();
+ this.labelIndex.unlock();
+ return newDataset;
+ }
+
+ /**
+ * Dumps the Dataset as a training/test file for SVMLight.
+ * class [fno:val]+
+ * The features must occur in consecutive order.
+ */
+ public void printSVMLightFormat() {
+ printSVMLightFormat(new PrintWriter(System.out));
+ }
+
+ /**
+ * Maps our labels to labels that are compatible with svm_light
+ * @return array of strings
+ */
+ public String[] makeSvmLabelMap() {
+ String[] labelMap = new String[numClasses()];
+ if (numClasses() > 2) {
+ for (int i = 0; i < labelMap.length; i++) {
+ labelMap[i] = String.valueOf((i + 1));
+ }
+ } else {
+ labelMap = new String[]{"+1", "-1"};
+ }
+ return labelMap;
+ }
+
+ // todo: Fix javadoc, have unit tested
+ /**
+ * Print SVM Light Format file.
+ *
+ * The following comments are no longer applicable because I am
+ * now printing out the exact labelID for each example. -Ramesh (nmramesh@cs.stanford.edu) 12/17/2009.
+ *
+ * If the Dataset has more than 2 classes, then it
+ * prints using the label index (+1) (for svm_struct). If it is 2 classes, then the labelIndex.get(0)
+ * is mapped to +1 and labelIndex.get(1) is mapped to -1 (for svm_light).
+ */
+
+ public void printSVMLightFormat(PrintWriter pw) {
+ //assumes each data item has a few features on, and sorts the feature keys while collecting the values in a counter
+
+ // old comment:
+ // the following code commented out by Ramesh (nmramesh@cs.stanford.edu) 12/17/2009.
+ // why not simply print the exact id of the label instead of mapping to some values??
+ // new comment:
+ // mihai: we NEED this, because svm_light has special conventions not supported by default by our labels,
+ // e.g., in a multiclass setting it assumes that labels start at 1 whereas our labels start at 0 (08/31/2010)
+ String[] labelMap = makeSvmLabelMap();
+
+ for (int i = 0; i < size; i++) {
+ RVFDatum d = getRVFDatum(i);
+ Counter c = d.asFeaturesCounter();
+ ClassicCounter printC = new ClassicCounter();
+ for (F f : c.keySet()) {
+ printC.setCount(featureIndex.indexOf(f), c.getCount(f));
+ }
+ Integer[] features = printC.keySet().toArray(new Integer[printC.keySet().size()]);
+ Arrays.sort(features);
+ StringBuilder sb = new StringBuilder();
+ sb.append(labelMap[labels[i]]).append(' ');
+ // sb.append(labels[i]).append(' '); // commented out by mihai: labels[i] breaks svm_light conventions!
+
+ /* Old code: assumes that F is Integer....
+ *
+ for (int f: features) {
+ sb.append((f + 1)).append(":").append(c.getCount(f)).append(" ");
+ }
+ */
+ //I think this is what was meant (using printC rather than c), but not sure
+ // ~Sarah Spikes (sdspikes@cs.stanford.edu)
+ for (int f: features) {
+ sb.append((f + 1)).append(':').append(printC.getCount(f)).append(' ');
+ }
+ pw.println(sb.toString());
+ }
+ }
+
+
+ public Iterator> iterator() {
+ return new Iterator>() {
+ private int id; // = 0;
+
+ public boolean hasNext() {
+ return id < size();
+ }
+
+ public RVFDatum next() {
+ if (id >= size()) {
+ throw new NoSuchElementException();
+ }
+ return getRVFDatum(id++);
+ }
+
+ public void remove() {
+ throw new UnsupportedOperationException();
+ }
+
+ };
+ }
+
+}
diff --git a/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/GeneralizedExpectationObjectiveFunction.java b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/GeneralizedExpectationObjectiveFunction.java
new file mode 100644
index 0000000..d5e4fc1
--- /dev/null
+++ b/corenlp-wrapper/stanford-corenlp-full-2013-04-04/src/edu/stanford/nlp/classify/GeneralizedExpectationObjectiveFunction.java
@@ -0,0 +1,225 @@
+package edu.stanford.nlp.classify;
+
+import edu.stanford.nlp.ling.Datum;
+import edu.stanford.nlp.ling.RVFDatum;
+import edu.stanford.nlp.math.ArrayMath;
+import edu.stanford.nlp.optimization.AbstractCachingDiffFunction;
+import edu.stanford.nlp.stats.ClassicCounter;
+import edu.stanford.nlp.stats.Counter;
+import edu.stanford.nlp.util.Generics;
+import edu.stanford.nlp.util.Triple;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+
+
+/**
+ * Implementation of Generalized Expectation Objective function for
+ * an I.I.D. log-linear model. See Mann and McCallum, ACL 2008.
+ * IMPORTANT: the current implementation is only correct as long as
+ * the labeled features passed to GE are binary.
+ * However, other features are allowed to be real valued.
+ * The original paper also discusses GE only for binary features.
+ *
+ * @author Ramesh Nallapati (nmramesh@cs.stanford.edu)
+ */
+
+public class GeneralizedExpectationObjectiveFunction extends AbstractCachingDiffFunction {
+
+ private final GeneralDataset labeledDataset;
+ private final List> unlabeledDataList;
+ private final List geFeatures;
+ private final LinearClassifier classifier;
+ private double[][] geFeature2EmpiricalDist; //empirical label distributions of each feature. Really final but java won't let us.
+ private List> geFeature2DatumList; //an inverted list of active unlabeled documents for each feature. Really final but java won't let us.
+
+ private final int numFeatures;
+ private final int numClasses;
+
+
+ @Override
+ public int domainDimension() {
+ return numFeatures * numClasses;
+ }
+
+ int classOf(int index) {
+ return index % numClasses;
+ }
+
+ int featureOf(int index) {
+ return index / numClasses;
+ }
+
+ protected int indexOf(int f, int c) {
+ return f * numClasses + c;
+ }
+
+ public double[][] to2D(double[] x) {
+ double[][] x2 = new double[numFeatures][numClasses];
+ for (int i = 0; i < numFeatures; i++) {
+ for (int j = 0; j < numClasses; j++) {
+ x2[i][j] = x[indexOf(i, j)];
+ }
+ }
+ return x2;
+ }
+
+ @Override
+ protected void calculate(double[] x) {
+ classifier.setWeights(to2D(x));
+ if (derivative == null) {
+ derivative = new double[x.length];
+ } else {
+ Arrays.fill(derivative, 0.0);
+ }
+ Counter> feature2classPairDerivatives = new ClassicCounter