pipeline/nltk/tokenize/stanford.py

# Natural Language Toolkit: Interface to the Stanford Tokenizer
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Steven Xu <[email protected]>
#
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT

import json
import os
import tempfile
import warnings
from subprocess import PIPE

from nltk.internals import _java_options, config_java, find_jar, java
from nltk.parse.corenlp import CoreNLPParser
from nltk.tokenize.api import TokenizerI

_stanford_url = "https://nlp.stanford.edu/software/tokenizer.shtml"


class StanfordTokenizer(TokenizerI):
    r"""
    Interface to the Stanford Tokenizer

    >>> from nltk.tokenize.stanford import StanfordTokenizer
    >>> s = "Good muffins cost $3.88\nin New York.  Please buy me\ntwo of them.\nThanks."
    >>> StanfordTokenizer().tokenize(s) # doctest: +SKIP
    ['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
    >>> s = "The colour of the wall is blue."
    >>> StanfordTokenizer(options={"americanize": True}).tokenize(s) # doctest: +SKIP
    ['The', 'color', 'of', 'the', 'wall', 'is', 'blue', '.']
    """

    _JAR = "stanford-postagger.jar"

    def __init__(
        self,
        path_to_jar=None,
        encoding="utf8",
        options=None,
        verbose=False,
        java_options="-mx1000m",
    ):
        # Raise deprecation warning.
        warnings.warn(
            str(
                "\nThe StanfordTokenizer will "
                "be deprecated in version 3.2.5.\n"
                "Please use \033[91mnltk.parse.corenlp.CoreNLPParser\033[0m instead.'"
            ),
            DeprecationWarning,
            stacklevel=2,
        )

        self._stanford_jar = find_jar(
            self._JAR,
            path_to_jar,
            env_vars=("STANFORD_POSTAGGER",),
            searchpath=(),
            url=_stanford_url,
            verbose=verbose,
        )

        self._encoding = encoding
        self.java_options = java_options

        options = {} if options is None else options
        self._options_cmd = ",".join(f"{key}={val}" for key, val in options.items())

    @staticmethod
    def _parse_tokenized_output(s):
        return s.splitlines()

    def tokenize(self, s):
        """
        Use stanford tokenizer's PTBTokenizer to tokenize multiple sentences.
        """
        cmd = ["edu.stanford.nlp.process.PTBTokenizer"]
        return self._parse_tokenized_output(self._execute(cmd, s))

    def _execute(self, cmd, input_, verbose=False):
        encoding = self._encoding
        cmd.extend(["-charset", encoding])
        _options_cmd = self._options_cmd
        if _options_cmd:
            cmd.extend(["-options", self._options_cmd])

        default_options = " ".join(_java_options)

        # Configure java.
        config_java(options=self.java_options, verbose=verbose)

        # Windows is incompatible with NamedTemporaryFile() without passing in delete=False.
        with tempfile.NamedTemporaryFile(mode="wb", delete=False) as input_file:
            # Write the actual sentences to the temporary input file
            if isinstance(input_, str) and encoding:
                input_ = input_.encode(encoding)
            input_file.write(input_)
            input_file.flush()

            cmd.append(input_file.name)

            # Run the tagger and get the output.
            stdout, stderr = java(
                cmd, classpath=self._stanford_jar, stdout=PIPE, stderr=PIPE
            )
            stdout = stdout.decode(encoding)

        os.unlink(input_file.name)

        # Return java configurations to their default values.
        config_java(options=default_options, verbose=False)

        return stdout