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corenlp-python 3.2.0-3

A Stanford Core NLP wrapper

# 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.

## Edited
   * Update to Stanford CoreNLP v3.2.0
   * Fix many bugs & improve performance
   * Using jsonrpclib for stability and performance
   * Can edit the constants as argument such as Stanford Core NLP directory
   * Adjust parameters not to timeout in high load
   * Fix a problem with long text input by Johannes Castner [stanford-corenlp-python](
   * Packaging

## Requirements
   * [pexpect](
   * [unidecode](
   * [jsonrpclib]( (optionally)

## Download and Usage

To use this program you must [download]( and unpack the zip file containing Stanford's CoreNLP package.  By default, `` 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
          cd corenlp-python

Then, to launch a server:

    python corenlp/

Optionally, you can specify a host or port:

    python corenlp/ -H -p 3456

That will run a public JSON-RPC server on port 3456.
And you can specify Stanford CoreNLP directory:

    python corenlp/ -S stanford-corenlp-full-2013-06-20/

Assuming you are running on port 8080 and CoreNLP directory is `stanford-corenlp-full-2013-06-20/` in current directory, the code in `` 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'CharacterOffsetBegin': u'6',
                                      u'CharacterOffsetEnd': u'11',
                                      u'Lemma': u'world',
                                      u'NamedEntityTag': u'O',
                                      u'PartOfSpeech': u'NN'}],
                                     {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'CharacterOffsetBegin': u'17',
                                      u'CharacterOffsetEnd': u'19',
                                      u'Lemma': u'be',
                                      u'NamedEntityTag': u'O',
                                      u'PartOfSpeech': u'VBZ'}],
                                     {u'CharacterOffsetBegin': u'20',
                                      u'CharacterOffsetEnd': u'22',
                                      u'Lemma': u'so',
                                      u'NamedEntityTag': u'O',
                                      u'PartOfSpeech': u'RB'}],
                                     {u'CharacterOffsetBegin': u'23',
                                      u'CharacterOffsetEnd': u'32',
                                      u'Lemma': u'beautiful',
                                      u'NamedEntityTag': u'O',
                                      u'PartOfSpeech': u'JJ'}],
                                     {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 []
   * Johannes Castner []
File Type Py Version Uploaded on Size
corenlp-python-3.2.0-3.tar.gz (md5) Source 2013-09-03 20KB
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