This a Python wrapper for Stanford University's NLP group's Java-based CoreNLP tools. It can either be imported as a module or run as an JSON-RPC server. Because it uses many large trained models (requiring 3GB RAM and usually a few minutes loading time), most applications will probably want to run it as a server.
It requires pexpect. Included dependencies are jsonrpc and python-progressbar.
There's not much to this script. I decided to create it after facing difficulties using the alternative ways to get Python to talk to Stanford's dependency parser. First, I had trouble initializing a JVM using JPypes on two different machines with stanford-parser-python, and Jython's lack of support for the Python modules I needed prevented a Jython solution.
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. I have only tested this on Core NLP tools version 1.0.2 released 2010-11-12.
You should have downloaded and unpacked the tgz file containing Stanford's CoreNLP package. Then copy all of the python files from this repository into the stanford-corenlp-2010-11-12 folder.
In other words:
sudo pip install pexpect
wget http://nlp.stanford.edu/software/stanford-corenlp-v1.0.2.tgz
tar xvfz stanford-corenlp-v1.0.2.tgz
cd stanford-corenlp-2010-11-12
git clone git://github.com/dasmith/stanford-corenlp-python.git
mv stanford-corenlp-python/* .
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"))
print "Result", result
Produces a list with a parsed dictionary for each sentence:
Result [{'text': 'hello world',
'tuples': [['amod', 'world', 'hello']],
'words': [['hello', {'NamedEntityTag': 'O', 'CharacterOffsetEnd': '5', 'CharacterOffsetBegin': '0', 'PartOfSpeech': 'JJ', 'Lemma': 'hello'}],
['world', {'NamedEntityTag': 'O', 'CharacterOffsetEnd': '11', 'CharacterOffsetBegin': '6', 'PartOfSpeech': 'NN', 'Lemma': 'world'}]]}]
To use it in a regular script or to edit/debug (since errors via RPC are opaque), load the module instead:
from corenlp import *
corenlp = StanfordCoreNLP()
corenlp.parse("Parse an imperative sentence, damnit!")
I added a function called parse_imperative that introduces a dummy pronoun to overcome the problems that dependency parsers have with imperative statements.
corenlp.parse("stop smoking")
>> [{"text": "stop smoking", "tuples": [["nn", "smoking", "stop"]], "words": [["stop", {"NamedEntityTag": "O", "CharacterOffsetEnd": "4", "Lemma": "stop", "PartOfSpeech": "NN", "CharacterOffsetBegin": "0"}], ["smoking", {"NamedEntityTag": "O", "CharacterOffsetEnd": "12", "Lemma": "smoking", "PartOfSpeech": "NN", "CharacterOffsetBegin": "5"}]]}]
corenlp.parse_imperative("stop smoking")
>> [{"text": "stop smoking", "tuples": [["xcomp", "stop", "smoking"]], "words": [["stop", {"NamedEntityTag": "O", "CharacterOffsetEnd": "8", "Lemma": "stop", "PartOfSpeech": "VBP", "CharacterOffsetBegin": "4"}], ["smoking", {"NamedEntityTag": "O", "CharacterOffsetEnd": "16", "Lemma": "smoke", "PartOfSpeech": "VBG", "CharacterOffsetBegin": "9"}]]}]
Only with the dummy pronoun does the parser correctly identify the first word, stop, to be a verb.
If you think there may be a problem with this wrapper, first ensure you can run the Java program:
java -cp stanford-corenlp-2010-11-12.jar:stanford-corenlp-models-2010-11-06.jar:xom-1.2.6.jar:xom.jar:jgraph.jar:jgrapht.jar -Xmx3g edu.stanford.nlp.pipeline.StanfordCoreNLP -props default.properties
- Adjust Char Offsets for
parse_imperativeto account for dummy pronoun. - Parse and resolve coreferences
- Mutex on parser