Source

corenlp-python /

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corenlp
sample_raw_text
37 B
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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 a python package, or run as a JSON-RPC server.

Updates from the original wrapper

  • Supports Stanford CoreNLP v3.x.x (compatible with recent versions)
  • Fixed many bugs & improved performance
  • Adjusted parameters not to timeout in high load
  • Using jsonrpclib for stability and performance
  • Batch parser for long text which supports sentiment analysis
  • Python 3 compatibility (thanks to Valentin Lorentz)
  • Packaging

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
# assuming the version 3.4.1 of Stanford CoreNLP
wget http://nlp.stanford.edu/software/stanford-corenlp-full-2014-08-27.zip
unzip stanford-corenlp-full-2014-08-27.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-2014-08-27/

Assuming you are running on port 8080 and CoreNLP directory is stanford-corenlp-full-2014-08-27/ in current directory, this wrapper supports recently version around of 3.4.1 which has same output format.

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-2014-08-27/"
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-2014-08-27/"
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)

Note

  • JSON-RPC server halts on large text. it maybe because of restriction of stdout, you should use the batch parser or an other wrapper.

  • JSON-RPC server doesn't support sentiment analysis tools because original CoreNLP tools don't output sentiment results to stdout yet (batch parser's output includes sentiment results retrieved from the original CoreNLP tools's XML output)

License

corenlp-python is licensed under the GNU General Public License (v2 or later). Note that this is the /full/ GPL, which allows many free uses, but not its use in distributed proprietary software.

Developer

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