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 having trouble initializing a JVM using JPypes on two different machines.
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
server = jsonrpc.ServerProxy(jsonrpc.JsonRpc20(),
jsonrpc.TransportTcpIp(addr=("127.0.0.1", 8080)))
result = 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": {"world": {"NamedEntityTag": "O",
"CharacterOffsetEnd": "11",
"Lemma": "world",
"PartOfSpeech": "NN",
"CharacterOffsetBegin": "6"},
"hello": {"NamedEntityTag": "O",
"CharacterOffsetEnd": "5",
"Lemma": "hello",
"PartOfSpeech": "JJ",
"CharacterOffsetBegin": "0"}}}]
To use it in a regular script or to edit/debug, load the module instead:
from corenlp import *
corenlp = StanfordCoreNLP()
corenlp.parse("Parse an imperative sentence, damnit!")
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