Stanford CoreNLP Python Interface

NOTE: This package is now deprecated. Please use the stanza <https://github.com/stanfordnlp/stanza>_ package instead.

.. image:: https://travis-ci.org/stanfordnlp/python-stanford-corenlp.svg?branch=master :target: https://travis-ci.org/stanfordnlp/python-stanford-corenlp

This package contains a python interface for Stanford CoreNLP <https://github.com/stanfordnlp/CoreNLP>_ that contains a reference implementation to interface with the Stanford CoreNLP server <https://stanfordnlp.github.io/CoreNLP/corenlp-server.html>_. The package also contains a base class to expose a python-based annotation provider (e.g. your favorite neural NER system) to the CoreNLP pipeline via a lightweight service.

To use the package, first download the official java CoreNLP release <https://stanfordnlp.github.io/CoreNLP/#download>_, unzip it, and define an environment variable :code:$CORENLP_HOME that points to the unzipped directory.

You can also install this package from PyPI <https://pypi.python.org/pypi/stanford-corenlp/>_ using :code:pip install stanford-corenlp

Command Line Usage

Probably the easiest way to use this package is through the annotate command-line utility::

usage: annotate [-h] [-i INPUT] [-o OUTPUT] [-f {json}]
                [-a ANNOTATORS [ANNOTATORS ...]] [-s] [-v] [-m MEMORY]
                [-p PROPS [PROPS ...]]

Annotate data

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input file to process; each line contains one document
                        (default: stdin)
  -o OUTPUT, --output OUTPUT
                        File to write annotations to (default: stdout)
  -f {json}, --format {json}
                        Output format
  -a ANNOTATORS [ANNOTATORS ...], --annotators ANNOTATORS [ANNOTATORS ...]
                        A list of annotators
  -s, --sentence-mode   Assume each line of input is a sentence.
  -v, --verbose-server  Server is made verbose
  -m MEMORY, --memory MEMORY
                        Memory to use for the server
  -p PROPS [PROPS ...], --props PROPS [PROPS ...]
                        Properties as a list of key=value pairs

We recommend using annotate in conjuction with the wonderful jq command to process the output. As an example, given a file with a sentence on each line, the following command produces an equivalent space-separated tokens::

cat file.txt | annotate -s -a tokenize | jq '[.tokens[].originalText]' > tokenized.txt

Annotation Server Usage

.. code-block:: python

import corenlp

text = "Chris wrote a simple sentence that he parsed with Stanford CoreNLP."

We assume that you've downloaded Stanford CoreNLP and defined an environment

variable $CORENLP_HOME that points to the unzipped directory.

The code below will launch StanfordCoreNLPServer in the background

and communicate with the server to annotate the sentence.

with corenlp.CoreNLPClient(annotators="tokenize ssplit pos lemma ner depparse".split()) as client: ann = client.annotate(text)

You can access annotations using ann.

sentence = ann.sentence[0]

The corenlp.to_text function is a helper function that

reconstructs a sentence from tokens.

assert corenlp.to_text(sentence) == text

You can access any property within a sentence.

print(sentence.text)

Likewise for tokens

token = sentence.token[0] print(token.lemma)

Use tokensregex patterns to find who wrote a sentence.

pattern = '([ner: PERSON]+) /wrote/ /an?/ []{0,3} /sentence|article/' matches = client.tokensregex(text, pattern)

sentences contains a list with matches for each sentence.

assert len(matches["sentences"]) == 1

length tells you whether or not there are any matches in this

assert matches["sentences"][0]["length"] == 1

You can access matches like most regex groups.

matches["sentences"][1]["0"]["text"] == "Chris wrote a simple sentence" matches["sentences"][1]["0"]["1"]["text"] == "Chris"

Use semgrex patterns to directly find who wrote what.

pattern = '{word:wrote} >nsubj {}=subject >dobj {}=object' matches = client.semgrex(text, pattern)

sentences contains a list with matches for each sentence.

assert len(matches["sentences"]) == 1

length tells you whether or not there are any matches in this

assert matches["sentences"][0]["length"] == 1

You can access matches like most regex groups.

matches["sentences"][1]["0"]["text"] == "wrote" matches["sentences"][1]["0"]["$subject"]["text"] == "Chris" matches["sentences"][1]["0"]["$object"]["text"] == "sentence"

See test_client.py and test_protobuf.py for more examples. Props to @dan-zheng for tokensregex/semgrex support.

Annotation Service Usage

NOTE: The annotation service allows users to provide a custom annotator to be used by the CoreNLP pipeline. Unfortunately, it relies on experimental code internal to the Stanford CoreNLP project is not yet available for public use.

.. code-block:: python

import corenlp
from .happyfuntokenizer import Tokenizer

class HappyFunTokenizer(Tokenizer, corenlp.Annotator):
    def __init__(self, preserve_case=False):
        Tokenizer.__init__(self, preserve_case)
        corenlp.Annotator.__init__(self)

    @property
    def name(self):
        """
        Name of the annotator (used by CoreNLP)
        """
        return "happyfun"

    @property
    def requires(self):
        """
        Requires has to specify all the annotations required before we
        are called.
        """
        return []

    @property
    def provides(self):
        """
        The set of annotations guaranteed to be provided when we are done.
        NOTE: that these annotations are either fully qualified Java
        class names or refer to nested classes of
        edu.stanford.nlp.ling.CoreAnnotations (as is the case below).
        """
        return ["TextAnnotation",
                "TokensAnnotation",
                "TokenBeginAnnotation",
                "TokenEndAnnotation",
                "CharacterOffsetBeginAnnotation",
                "CharacterOffsetEndAnnotation",
               ]

    def annotate(self, ann):
        """
        @ann: is a protobuf annotation object.
        Actually populate @ann with tokens.
        """
        buf, beg_idx, end_idx = ann.text.lower(), 0, 0
        for i, word in enumerate(self.tokenize(ann.text)):
            token = ann.sentencelessToken.add()
            # These are the bare minimum required for the TokenAnnotation
            token.word = word
            token.tokenBeginIndex = i
            token.tokenEndIndex = i+1

            # Seek into the txt until you can find this word.
            try:
                # Try to update beginning index
                beg_idx = buf.index(word, beg_idx)
            except ValueError:
                # Give up -- this will be something random
                end_idx = beg_idx + len(word)

            token.beginChar = beg_idx
            token.endChar = end_idx

            beg_idx, end_idx = end_idx, end_idx

annotator = HappyFunTokenizer()
# Calling .start() will launch the annotator as a service running on
# port 8432 by default.
annotator.start()

# annotator.properties contains all the right properties for
# Stanford CoreNLP to use this annotator. 
with corenlp.CoreNLPClient(properties=annotator.properties, annotators="happyfun ssplit pos".split()) as client:
    ann = client.annotate("RT @ #happyfuncoding: this is a typical Twitter tweet :-)")

    tokens = [t.word for t in ann.sentence[0].token]
    print(tokens)

See test_annotator.py for more examples.