# Natural Language Toolkit: Interface to the Stanford Parser
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Steven Xu <xxu@student.unimelb.edu.au>
#
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
import os
import tempfile
import warnings
from subprocess import PIPE
from nltk.internals import (
_java_options,
config_java,
find_jar_iter,
find_jars_within_path,
java,
)
from nltk.parse.api import ParserI
from nltk.parse.dependencygraph import DependencyGraph
from nltk.tree import Tree
_stanford_url = "https://nlp.stanford.edu/software/lex-parser.shtml"
[docs]class GenericStanfordParser(ParserI):
"""Interface to the Stanford Parser"""
_MODEL_JAR_PATTERN = r"stanford-parser-(\d+)(\.(\d+))+-models\.jar"
_JAR = r"stanford-parser\.jar"
_MAIN_CLASS = "edu.stanford.nlp.parser.lexparser.LexicalizedParser"
_USE_STDIN = False
_DOUBLE_SPACED_OUTPUT = False
[docs] def __init__(
self,
path_to_jar=None,
path_to_models_jar=None,
model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz",
encoding="utf8",
verbose=False,
java_options="-mx4g",
corenlp_options="",
):
# find the most recent code and model jar
stanford_jar = max(
find_jar_iter(
self._JAR,
path_to_jar,
env_vars=("STANFORD_PARSER", "STANFORD_CORENLP"),
searchpath=(),
url=_stanford_url,
verbose=verbose,
is_regex=True,
),
key=lambda model_path: os.path.dirname(model_path),
)
model_jar = max(
find_jar_iter(
self._MODEL_JAR_PATTERN,
path_to_models_jar,
env_vars=("STANFORD_MODELS", "STANFORD_CORENLP"),
searchpath=(),
url=_stanford_url,
verbose=verbose,
is_regex=True,
),
key=lambda model_path: os.path.dirname(model_path),
)
# self._classpath = (stanford_jar, model_jar)
# Adding logging jar files to classpath
stanford_dir = os.path.split(stanford_jar)[0]
self._classpath = tuple([model_jar] + find_jars_within_path(stanford_dir))
self.model_path = model_path
self._encoding = encoding
self.corenlp_options = corenlp_options
self.java_options = java_options
def _parse_trees_output(self, output_):
res = []
cur_lines = []
cur_trees = []
blank = False
for line in output_.splitlines(False):
if line == "":
if blank:
res.append(iter(cur_trees))
cur_trees = []
blank = False
elif self._DOUBLE_SPACED_OUTPUT:
cur_trees.append(self._make_tree("\n".join(cur_lines)))
cur_lines = []
blank = True
else:
res.append(iter([self._make_tree("\n".join(cur_lines))]))
cur_lines = []
else:
cur_lines.append(line)
blank = False
return iter(res)
[docs] def parse_sents(self, sentences, verbose=False):
"""
Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
list where each sentence is a list of words.
Each sentence will be automatically tagged with this StanfordParser instance's
tagger.
If whitespaces exists inside a token, then the token will be treated as
separate tokens.
:param sentences: Input sentences to parse
:type sentences: list(list(str))
:rtype: iter(iter(Tree))
"""
cmd = [
self._MAIN_CLASS,
"-model",
self.model_path,
"-sentences",
"newline",
"-outputFormat",
self._OUTPUT_FORMAT,
"-tokenized",
"-escaper",
"edu.stanford.nlp.process.PTBEscapingProcessor",
]
return self._parse_trees_output(
self._execute(
cmd, "\n".join(" ".join(sentence) for sentence in sentences), verbose
)
)
[docs] def raw_parse(self, sentence, verbose=False):
"""
Use StanfordParser to parse a sentence. Takes a sentence as a string;
before parsing, it will be automatically tokenized and tagged by
the Stanford Parser.
:param sentence: Input sentence to parse
:type sentence: str
:rtype: iter(Tree)
"""
return next(self.raw_parse_sents([sentence], verbose))
[docs] def raw_parse_sents(self, sentences, verbose=False):
"""
Use StanfordParser to parse multiple sentences. Takes multiple sentences as a
list of strings.
Each sentence will be automatically tokenized and tagged by the Stanford Parser.
:param sentences: Input sentences to parse
:type sentences: list(str)
:rtype: iter(iter(Tree))
"""
cmd = [
self._MAIN_CLASS,
"-model",
self.model_path,
"-sentences",
"newline",
"-outputFormat",
self._OUTPUT_FORMAT,
]
return self._parse_trees_output(
self._execute(cmd, "\n".join(sentences), verbose)
)
[docs] def tagged_parse(self, sentence, verbose=False):
"""
Use StanfordParser to parse a sentence. Takes a sentence as a list of
(word, tag) tuples; the sentence must have already been tokenized and
tagged.
:param sentence: Input sentence to parse
:type sentence: list(tuple(str, str))
:rtype: iter(Tree)
"""
return next(self.tagged_parse_sents([sentence], verbose))
[docs] def tagged_parse_sents(self, sentences, verbose=False):
"""
Use StanfordParser to parse multiple sentences. Takes multiple sentences
where each sentence is a list of (word, tag) tuples.
The sentences must have already been tokenized and tagged.
:param sentences: Input sentences to parse
:type sentences: list(list(tuple(str, str)))
:rtype: iter(iter(Tree))
"""
tag_separator = "/"
cmd = [
self._MAIN_CLASS,
"-model",
self.model_path,
"-sentences",
"newline",
"-outputFormat",
self._OUTPUT_FORMAT,
"-tokenized",
"-tagSeparator",
tag_separator,
"-tokenizerFactory",
"edu.stanford.nlp.process.WhitespaceTokenizer",
"-tokenizerMethod",
"newCoreLabelTokenizerFactory",
]
# We don't need to escape slashes as "splitting is done on the last instance of the character in the token"
return self._parse_trees_output(
self._execute(
cmd,
"\n".join(
" ".join(tag_separator.join(tagged) for tagged in sentence)
for sentence in sentences
),
verbose,
)
)
def _execute(self, cmd, input_, verbose=False):
encoding = self._encoding
cmd.extend(["-encoding", encoding])
if self.corenlp_options:
cmd.extend(self.corenlp_options.split())
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()
# Run the tagger and get the output.
if self._USE_STDIN:
input_file.seek(0)
stdout, stderr = java(
cmd,
classpath=self._classpath,
stdin=input_file,
stdout=PIPE,
stderr=PIPE,
)
else:
cmd.append(input_file.name)
stdout, stderr = java(
cmd, classpath=self._classpath, stdout=PIPE, stderr=PIPE
)
stdout = stdout.replace(b"\xc2\xa0", b" ")
stdout = stdout.replace(b"\x00\xa0", b" ")
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
[docs]class StanfordParser(GenericStanfordParser):
"""
>>> parser=StanfordParser(
... model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"
... ) # doctest: +SKIP
>>> list(parser.raw_parse("the quick brown fox jumps over the lazy dog")) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']),
Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']),
Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])])]
>>> sum([list(dep_graphs) for dep_graphs in parser.raw_parse_sents((
... "the quick brown fox jumps over the lazy dog",
... "the quick grey wolf jumps over the lazy fox"
... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('ROOT', [Tree('NP', [Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['brown']),
Tree('NN', ['fox'])]), Tree('NP', [Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']),
Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])])])]), Tree('ROOT', [Tree('NP',
[Tree('NP', [Tree('DT', ['the']), Tree('JJ', ['quick']), Tree('JJ', ['grey']), Tree('NN', ['wolf'])]), Tree('NP',
[Tree('NP', [Tree('NNS', ['jumps'])]), Tree('PP', [Tree('IN', ['over']), Tree('NP', [Tree('DT', ['the']),
Tree('JJ', ['lazy']), Tree('NN', ['fox'])])])])])])]
>>> sum([list(dep_graphs) for dep_graphs in parser.parse_sents((
... "I 'm a dog".split(),
... "This is my friends ' cat ( the tabby )".split(),
... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('ROOT', [Tree('S', [Tree('NP', [Tree('PRP', ['I'])]), Tree('VP', [Tree('VBP', ["'m"]),
Tree('NP', [Tree('DT', ['a']), Tree('NN', ['dog'])])])])]), Tree('ROOT', [Tree('S', [Tree('NP',
[Tree('DT', ['This'])]), Tree('VP', [Tree('VBZ', ['is']), Tree('NP', [Tree('NP', [Tree('NP', [Tree('PRP$', ['my']),
Tree('NNS', ['friends']), Tree('POS', ["'"])]), Tree('NN', ['cat'])]), Tree('PRN', [Tree('-LRB-', [Tree('', []),
Tree('NP', [Tree('DT', ['the']), Tree('NN', ['tabby'])]), Tree('-RRB-', [])])])])])])])]
>>> sum([list(dep_graphs) for dep_graphs in parser.tagged_parse_sents((
... (
... ("The", "DT"),
... ("quick", "JJ"),
... ("brown", "JJ"),
... ("fox", "NN"),
... ("jumped", "VBD"),
... ("over", "IN"),
... ("the", "DT"),
... ("lazy", "JJ"),
... ("dog", "NN"),
... (".", "."),
... ),
... ))],[]) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('ROOT', [Tree('S', [Tree('NP', [Tree('DT', ['The']), Tree('JJ', ['quick']), Tree('JJ', ['brown']),
Tree('NN', ['fox'])]), Tree('VP', [Tree('VBD', ['jumped']), Tree('PP', [Tree('IN', ['over']), Tree('NP',
[Tree('DT', ['the']), Tree('JJ', ['lazy']), Tree('NN', ['dog'])])])]), Tree('.', ['.'])])])]
"""
_OUTPUT_FORMAT = "penn"
[docs] def __init__(self, *args, **kwargs):
warnings.warn(
"The StanfordParser will be deprecated\n"
"Please use \033[91mnltk.parse.corenlp.CoreNLPParser\033[0m instead.",
DeprecationWarning,
stacklevel=2,
)
super().__init__(*args, **kwargs)
def _make_tree(self, result):
return Tree.fromstring(result)
[docs]class StanfordDependencyParser(GenericStanfordParser):
"""
>>> dep_parser=StanfordDependencyParser(
... model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"
... ) # doctest: +SKIP
>>> [parse.tree() for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy'])])]
>>> [list(parse.triples()) for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE +SKIP
[[((u'jumps', u'VBZ'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det', (u'The', u'DT')),
((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'), u'amod', (u'brown', u'JJ')),
((u'jumps', u'VBZ'), u'nmod', (u'dog', u'NN')), ((u'dog', u'NN'), u'case', (u'over', u'IN')),
((u'dog', u'NN'), u'det', (u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ'))]]
>>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.raw_parse_sents((
... "The quick brown fox jumps over the lazy dog.",
... "The quick grey wolf jumps over the lazy fox."
... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy'])]),
Tree('jumps', [Tree('wolf', ['The', 'quick', 'grey']), Tree('fox', ['over', 'the', 'lazy'])])]
>>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.parse_sents((
... "I 'm a dog".split(),
... "This is my friends ' cat ( the tabby )".split(),
... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('dog', ['I', "'m", 'a']), Tree('cat', ['This', 'is', Tree('friends', ['my', "'"]), Tree('tabby', ['the'])])]
>>> sum([[list(parse.triples()) for parse in dep_graphs] for dep_graphs in dep_parser.tagged_parse_sents((
... (
... ("The", "DT"),
... ("quick", "JJ"),
... ("brown", "JJ"),
... ("fox", "NN"),
... ("jumped", "VBD"),
... ("over", "IN"),
... ("the", "DT"),
... ("lazy", "JJ"),
... ("dog", "NN"),
... (".", "."),
... ),
... ))],[]) # doctest: +NORMALIZE_WHITESPACE +SKIP
[[((u'jumped', u'VBD'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det', (u'The', u'DT')),
((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'), u'amod', (u'brown', u'JJ')),
((u'jumped', u'VBD'), u'nmod', (u'dog', u'NN')), ((u'dog', u'NN'), u'case', (u'over', u'IN')),
((u'dog', u'NN'), u'det', (u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ'))]]
"""
_OUTPUT_FORMAT = "conll2007"
[docs] def __init__(self, *args, **kwargs):
warnings.warn(
"The StanfordDependencyParser will be deprecated\n"
"Please use \033[91mnltk.parse.corenlp.CoreNLPDependencyParser\033[0m instead.",
DeprecationWarning,
stacklevel=2,
)
super().__init__(*args, **kwargs)
def _make_tree(self, result):
return DependencyGraph(result, top_relation_label="root")
[docs]class StanfordNeuralDependencyParser(GenericStanfordParser):
"""
>>> from nltk.parse.stanford import StanfordNeuralDependencyParser # doctest: +SKIP
>>> dep_parser=StanfordNeuralDependencyParser(java_options='-mx4g')# doctest: +SKIP
>>> [parse.tree() for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over', 'the', 'lazy']), '.'])]
>>> [list(parse.triples()) for parse in dep_parser.raw_parse("The quick brown fox jumps over the lazy dog.")] # doctest: +NORMALIZE_WHITESPACE +SKIP
[[((u'jumps', u'VBZ'), u'nsubj', (u'fox', u'NN')), ((u'fox', u'NN'), u'det',
(u'The', u'DT')), ((u'fox', u'NN'), u'amod', (u'quick', u'JJ')), ((u'fox', u'NN'),
u'amod', (u'brown', u'JJ')), ((u'jumps', u'VBZ'), u'nmod', (u'dog', u'NN')),
((u'dog', u'NN'), u'case', (u'over', u'IN')), ((u'dog', u'NN'), u'det',
(u'the', u'DT')), ((u'dog', u'NN'), u'amod', (u'lazy', u'JJ')), ((u'jumps', u'VBZ'),
u'punct', (u'.', u'.'))]]
>>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.raw_parse_sents((
... "The quick brown fox jumps over the lazy dog.",
... "The quick grey wolf jumps over the lazy fox."
... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('jumps', [Tree('fox', ['The', 'quick', 'brown']), Tree('dog', ['over',
'the', 'lazy']), '.']), Tree('jumps', [Tree('wolf', ['The', 'quick', 'grey']),
Tree('fox', ['over', 'the', 'lazy']), '.'])]
>>> sum([[parse.tree() for parse in dep_graphs] for dep_graphs in dep_parser.parse_sents((
... "I 'm a dog".split(),
... "This is my friends ' cat ( the tabby )".split(),
... ))], []) # doctest: +NORMALIZE_WHITESPACE +SKIP
[Tree('dog', ['I', "'m", 'a']), Tree('cat', ['This', 'is', Tree('friends',
['my', "'"]), Tree('tabby', ['-LRB-', 'the', '-RRB-'])])]
"""
_OUTPUT_FORMAT = "conll"
_MAIN_CLASS = "edu.stanford.nlp.pipeline.StanfordCoreNLP"
_JAR = r"stanford-corenlp-(\d+)(\.(\d+))+\.jar"
_MODEL_JAR_PATTERN = r"stanford-corenlp-(\d+)(\.(\d+))+-models\.jar"
_USE_STDIN = True
_DOUBLE_SPACED_OUTPUT = True
[docs] def __init__(self, *args, **kwargs):
warnings.warn(
"The StanfordNeuralDependencyParser will be deprecated\n"
"Please use \033[91mnltk.parse.corenlp.CoreNLPDependencyParser\033[0m instead.",
DeprecationWarning,
stacklevel=2,
)
super().__init__(*args, **kwargs)
self.corenlp_options += "-annotators tokenize,ssplit,pos,depparse"
[docs] def tagged_parse_sents(self, sentences, verbose=False):
"""
Currently unimplemented because the neural dependency parser (and
the StanfordCoreNLP pipeline class) doesn't support passing in pre-
tagged tokens.
"""
raise NotImplementedError(
"tagged_parse[_sents] is not supported by "
"StanfordNeuralDependencyParser; use "
"parse[_sents] or raw_parse[_sents] instead."
)
def _make_tree(self, result):
return DependencyGraph(result, top_relation_label="ROOT")