Source

corenlp-python / README.md

A Python wrapper for the Java Stanford Core NLP tools


This is a fork of Dustin Smith's stanford-corenlp-python, a Python interface to Stanford CoreNLP. It can either use as python package, or run as a JSON-RPC server.

Updates from the original

  • Update to Stanford CoreNLP v3.3.0
  • Fix many bugs & improve performance
  • Using jsonrpclib for stability and performance
  • Can edit constants as an argument such as Stanford Core NLP directory
  • Adjust parameters not to timeout in high load
  • File input feature added by Johannes Castner stanford-corenlp-python
  • Packaging

Progress of the sentiment tool support

  • File input - OK
  • Python package and JSON-RPC server - progressing

If you want to try the python interface to the sentiment tool, you have to comment out following line in corenlp/default.properties

annotators = tokenize, ssplit, pos, lemma, ner, parse, dcoref, sentiment

This feature is contributed by "Seongtaek Lim"

Requirements

Download and Usage

To use this program you must 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 pexpect unidecode jsonrpclib   # jsonrpclib is optional
git clone https://bitbucket.org/torotoki/corenlp-python.git
  cd corenlp-python
wget http://nlp.stanford.edu/software/stanford-corenlp-full-2013-11-12.zip
unzip stanford-corenlp-full-2013-11-12.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-2013-11-12/

Assuming you are running on port 8080 and CoreNLP directory is stanford-corenlp-full-2013-11-12/ 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]]]]}

Not to use JSON-RPC, load the module instead:

from corenlp import StanfordCoreNLP
corenlp_dir = "stanford-corenlp-full-2013-06-20/"
corenlp = StanfordCoreNLP(corenlp_dir)  # wait a few minutes...
corenlp.raw_parse("Parse it")

If you need to parse long texts (more than 30-50 sentences), you must use a batch_parse function. It reads text files from input directory and returns a generator object of dictionaries parsed each file results:

from corenlp import batch_parse
corenlp_dir = "stanford-corenlp-full-2013-06-20/"
raw_text_directory = "sample_raw_text/"
parsed = batch_parse(raw_text_directory, corenlp_dir)  # It returns a generator object
print parsed  #=> [{'coref': ..., 'sentences': ..., 'file_name': 'new_sample.txt'}]

The function uses XML output feature of Stanford CoreNLP, and you can take all information by raw_output option. If true, CoreNLP's XML is returned as a dictionary without converting the format.

parsed = batch_parse(raw_text_directory, corenlp_dir, raw_output=True)

(note: The function requires xmltodict now, you should install it by sudo pip install xmltodict)

Developer

  • Hiroyoshi Komatsu [hiroyoshi.komat@gmail.com]
  • Johannes Castner [jac2130@columbia.edu]
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