This is a Python wrapper for the Stanford CoreNLP library, allowing sentence splitting, POS/NER, and parse annotations. (Coreference is not supported currently.) It runs the Java software as a subprocess and communicates over sockets. This library handles timeouts and some process management (restarting the server if it seems to have crashed).

Alternatives you may want to consider:

• https://bitbucket.org/torotoki/corenlp-python
• https://github.com/dasmith/stanford-corenlp-python
• http://py4j.sourceforge.net/

This wrapper's defaults assume CoreNLP 3.4. It uses whatever the CoreNLP default settings are, but they can be overriden with a configuration file. The included sample.ini configuration file, for example, runs with the shift-reduce parser (and requires the appropriate model file to be downloaded into corenlp_libdir.)

Install

You can install the program using something like:

git clone https://github.com/brendano/stanford-corepywrapper
pip install stanford-corepywrapper

Usage

The basic arguments to open a server are (1) the pipeline type (see javasrc/corenlp/Parse.java for the list of possible ones), and (2) the directory that contains the CoreNLP jar files. Here we assume the program has been installed using pip install.

>>> from stanford_corenlp_pywrapper import sockwrap
>>> p=sockwrap.SockWrap("pos", corenlp_libdir="stanford-corenlp-full-2014-06-16")

INFO:StanfordSocketWrap:Starting pipe subprocess, and waiting for signal it's ready, with command:  exec java -Xmx4g -cp /Users/brendano/sw/nlp/stanford-pywrapper/lib/piperunner.jar:/Users/brendano/sw/nlp/stanford-pywrapper/lib/guava-13.0.1.jar:/Users/brendano/sw/nlp/stanford-pywrapper/lib/jackson-all-1.9.11.jar:stanford-corenlp-full-2014-06-16/stanford-corenlp-3.4.jar:stanford-corenlp-full-2014-06-16/stanford-corenlp-3.4-models.jar:stanford-corenlp-full-2014-06-16/stanford-srparser-2014-07-01-models.jar     corenlp.PipeCommandRunner --server 12340  --mode pos
[Server] Using mode type: pos
Adding annotator tokenize
Adding annotator ssplit
Adding annotator pos
Reading POS tagger model from edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger ... INFO:StanfordSocketWrap:Waiting for startup: ping got exception: <class 'socket.error'> [Errno 61] Connection refused
done [1.6 sec].
Adding annotator lemma
[Server] Started socket server on port 12340
INFO:StanfordSocketWrap:Successful ping. The server has started.
INFO:StanfordSocketWrap:Subprocess is ready.

The return values are JSON-safe data structures (in fact, the python<->java communication is a JSON protocol).

>>> p.parse_doc("hello world. how are you?")
{u'sentences': 
    [
        {u'tokens': [u'hello', u'world', u'.'],
         u'lemmas': [u'hello', u'world', u'.'],
         u'pos': [u'UH', u'NN', u'.'],
         u'char_offsets': [[0, 5], [6, 11], [11, 12]]
        },
        {u'tokens': [u'how', u'are', u'you', u'?'],
         u'lemmas': [u'how', u'be', u'you', u'?'],
         u'pos': [u'WRB', u'VBP', u'PRP', u'.'],
         u'char_offsets': [[13, 16], [17, 20], [21, 24], [24, 25]]
        }
    ]
}

Here is how to specify a configuration file:

>>> p=sockwrap.SockWrap("justparse", configfile='sample.ini')
>>> p.parse_doc("hello world. how are you?")
{u'sentences':
    [
        {u'tokens': [u'hello', u'world', u'.'],
         u'char_offsets': [[0, 5], [6, 11], [11, 12]],
         u'pos': [u'UH', u'NN', u'.'],
         u'parse': u'(ROOT (S (VP (NP (INTJ (UH hello)) (NP (NN world)))) (. .)))'
        },
        {u'tokens': [u'how', u'are', u'you', u'?'],
         u'char_offsets': [[13, 16], [17, 20], [21, 24], [24, 25]],
         u'pos': [u'WRB', u'VBP', u'PRP', u'.'],
         u'parse': u'(ROOT (SBARQ (WHADVP (WRB how)) (SQ (VBP are) (NP (PRP you))) (. ?)))'
        }
    ]
}

Notes

• A pipeline type is a notion only in our server, not in CoreNLP itself. (TODO: we should get rid of these eventually?) Our server uses the pipeline type in order to know what annotators to set, and what annotations to return. (It appears that the annotators can be overriden with a configuration file.)

• The configuration files are Java properties files, which I think are the .ini format, but am not sure. It used to be that CoreNLP came with some sample versions of these, but I can't find any at the moment. (TOCONSIDER: maybe we should abolish this in the interface and use a Python dict instead?)

• Some of the output messages are stderr from the CoreNLP subprocess. Everything starting with INFO: or WARNING: is from the Python logging system, in the parent process. Messages starting with [Server] are from the Java subprocess, in our server code (but not from Stanford CoreNLP).

• To use a different CoreNLP version, make sure the corenlp_libdir and corenlp_jars parameters are correct. If a future CoreNLP breaks binary (Java API) compatibility, you'll have to edit the Java server code and re-compile piperunner.jar via ./build.sh.

• If you want to run multiple instances on the same machine, make sure each SockWrap instance has a unique port number. (TOCONSIDER: use a different mechanism that doesn't require port numbers.)

• An important to-do is to test this code's robustness in a variety of situations. Bugs will probably occur when processing larger and larger datasets, and I don't know the right policies to have for timeouts, when to give up and restart after a timeout, and whether to re-try analyzing a document or give up and move on (because state dependence and "killer documents" screw all this up in different ways). Thanks to John Beieler for testing on the PETRARCH news analysis pipeline.

Testing

There are some pytest-style tests, though they're incomplete. Run:

py.test -v sockwrap.py

License etc.

Copyright Brendan O'Connor (http://brenocon.com).
License GPL version 2 or later.

Some Java files were copied from github.com/brendano/myutil.