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. The repository includes and uses code from jsonrpc and python-progressbar.

There's not much to this script. I decided to create it after having problems using other Python wrappers to Stanford's dependency parser. First the JPypes approach used in stanford-parser-python had trouble initializing a JVM on two separate computers. Next, I discovered I could not use a Jython solution because the Python modules I needed did not work in Jython.

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 

That returns a list containing a dictionary for each sentence, with keys text, tuples of the dependencies, and words:

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 it (because errors via RPC are opaque), load the module instead:

from corenlp import * corenlp = StanfordCoreNLP() # wait a few minutes... 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 

Then, send me (Dustin Smith) a message on GitHub or through email (contact information is available on my webpage.

• Adjust Char Offsets for parse_imperative to account for dummy pronoun.
• Parse and resolve coreferences
• Mutex on parser