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laywerrobot/lib/python3.6/site-packages/nltk/parse/corenlp.py

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2020-08-27 21:55:39 +02:00
# -*- coding: utf-8 -*-
# Natural Language Toolkit: Interface to the CoreNLP REST API.
#
# Copyright (C) 2001-2016 NLTK Project
# Author: Dmitrijs Milajevs <dimazest@gmail.com>
#
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import unicode_literals
import re
import json
import time
import socket
from nltk.internals import find_jar_iter, config_java, java, _java_options
from nltk.tag.api import TaggerI
from nltk.parse.api import ParserI
from nltk.tokenize.api import TokenizerI
from nltk.parse.dependencygraph import DependencyGraph
from nltk.tree import Tree
_stanford_url = 'http://stanfordnlp.github.io/CoreNLP/'
class CoreNLPServerError(EnvironmentError):
"""Exceptions associated with the Core NLP server."""
def try_port(port=0):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(('', port))
p = sock.getsockname()[1]
sock.close()
return p
class CoreNLPServer(object):
_MODEL_JAR_PATTERN = r'stanford-corenlp-(\d+)\.(\d+)\.(\d+)-models\.jar'
_JAR = r'stanford-corenlp-(\d+)\.(\d+)\.(\d+)\.jar'
def __init__(
self, path_to_jar=None, path_to_models_jar=None, verbose=False,
java_options=None, corenlp_options=None, port=None,
):
if corenlp_options is None:
corenlp_options = [
'-preload', 'tokenize,ssplit,pos,lemma,parse,depparse',
]
jars = list(find_jar_iter(
self._JAR,
path_to_jar,
env_vars=('CORENLP', ),
searchpath=(),
url=_stanford_url,
verbose=verbose,
is_regex=True,
))
# find the most recent code and model jar
stanford_jar = max(
jars,
key=lambda model_name: re.match(self._JAR, model_name)
)
if port is None:
try:
port = try_port(9000)
except socket.error:
port = try_port()
corenlp_options.append(str(port))
else:
try_port(port)
self.url = 'http://localhost:{}'.format(port)
model_jar = max(
find_jar_iter(
self._MODEL_JAR_PATTERN,
path_to_models_jar,
env_vars=('CORENLP_MODELS', ),
searchpath=(),
url=_stanford_url,
verbose=verbose,
is_regex=True,
),
key=lambda model_name: re.match(self._MODEL_JAR_PATTERN, model_name)
)
self.verbose = verbose
self._classpath = stanford_jar, model_jar
self.corenlp_options = corenlp_options
self.java_options = java_options or ['-mx2g']
def start(self):
import requests
cmd = ['edu.stanford.nlp.pipeline.StanfordCoreNLPServer']
if self.corenlp_options:
cmd.extend(self.corenlp_options)
# Configure java.
default_options = ' '.join(_java_options)
config_java(options=self.java_options, verbose=self.verbose)
try:
# TODO: it's probably a bad idea to pipe stdout, as it will
# accumulate when lots of text is being parsed.
self.popen = java(
cmd,
classpath=self._classpath,
blocking=False,
stdout='pipe',
stderr='pipe',
)
finally:
# Return java configurations to their default values.
config_java(options=default_options, verbose=self.verbose)
# Check that the server is istill running.
returncode = self.popen.poll()
if returncode is not None:
_, stderrdata = self.popen.communicate()
raise CoreNLPServerError(
returncode,
'Could not start the server. '
'The error was: {}'.format(stderrdata.decode('ascii'))
)
for i in range(30):
try:
response = requests.get(requests.compat.urljoin(self.url, 'live'))
except requests.exceptions.ConnectionError:
time.sleep(1)
else:
if response.ok:
break
else:
raise CoreNLPServerError(
'Could not connect to the server.'
)
for i in range(60):
try:
response = requests.get(requests.compat.urljoin(self.url, 'ready'))
except requests.exceptions.ConnectionError:
time.sleep(1)
else:
if response.ok:
break
else:
raise CoreNLPServerError(
'The server is not ready.'
)
def stop(self):
self.popen.terminate()
self.popen.wait()
def __enter__(self):
self.start()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop()
return False
class GenericCoreNLPParser(ParserI, TokenizerI, TaggerI):
"""Interface to the CoreNLP Parser."""
def __init__(self, url='http://localhost:9000', encoding='utf8', tagtype=None):
import requests
self.url = url
self.encoding = encoding
if tagtype not in ['pos', 'ner', None]:
raise ValueError("tagtype must be either 'pos', 'ner' or None")
self.tagtype = tagtype
self.session = requests.Session()
def parse_sents(self, sentences, *args, **kwargs):
"""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
CoreNLPParser instance's tagger.
If a whitespace exists inside a token, then the token will be treated as
several tokens.
:param sentences: Input sentences to parse
:type sentences: list(list(str))
:rtype: iter(iter(Tree))
"""
# Converting list(list(str)) -> list(str)
sentences = (' '.join(words) for words in sentences)
return self.raw_parse_sents(sentences, *args, **kwargs)
def raw_parse(self, sentence, properties=None, *args, **kwargs):
"""Parse a sentence.
Takes a sentence as a string; before parsing, it will be automatically
tokenized and tagged by the CoreNLP Parser.
:param sentence: Input sentence to parse
:type sentence: str
:rtype: iter(Tree)
"""
default_properties = {
'tokenize.whitespace': 'false',
}
default_properties.update(properties or {})
return next(
self.raw_parse_sents(
[sentence],
properties=default_properties,
*args,
**kwargs
)
)
def api_call(self, data, properties=None):
default_properties = {
'outputFormat': 'json',
'annotators': 'tokenize,pos,lemma,ssplit,{parser_annotator}'.format(
parser_annotator=self.parser_annotator,
),
}
default_properties.update(properties or {})
response = self.session.post(
self.url,
params={
'properties': json.dumps(default_properties),
},
data=data.encode(self.encoding),
timeout=60,
)
response.raise_for_status()
return response.json()
def raw_parse_sents(
self,
sentences,
verbose=False,
properties=None,
*args,
**kwargs
):
"""Parse multiple sentences.
Takes multiple sentences as a list of strings. Each sentence will be
automatically tokenized and tagged.
:param sentences: Input sentences to parse.
:type sentences: list(str)
:rtype: iter(iter(Tree))
"""
default_properties = {
# Only splits on '\n', never inside the sentence.
'ssplit.ssplit.eolonly': 'true',
}
default_properties.update(properties or {})
"""
for sentence in sentences:
parsed_data = self.api_call(sentence, properties=default_properties)
assert len(parsed_data['sentences']) == 1
for parse in parsed_data['sentences']:
tree = self.make_tree(parse)
yield iter([tree])
"""
parsed_data = self.api_call('\n'.join(sentences), properties=default_properties)
for parsed_sent in parsed_data['sentences']:
tree = self.make_tree(parsed_sent)
yield iter([tree])
def parse_text(self, text, *args, **kwargs):
"""Parse a piece of text.
The text might contain several sentences which will be split by CoreNLP.
:param str text: text to be split.
:returns: an iterable of syntactic structures. # TODO: should it be an iterable of iterables?
"""
parsed_data = self.api_call(text, *args, **kwargs)
for parse in parsed_data['sentences']:
yield self.make_tree(parse)
def tokenize(self, text, properties=None):
"""Tokenize a string of text.
>>> parser = CoreNLPParser(url='http://localhost:9000')
>>> text = 'Good muffins cost $3.88\\nin New York. Please buy me\\ntwo of them.\\nThanks.'
>>> list(parser.tokenize(text))
['Good', 'muffins', 'cost', '$', '3.88', 'in', 'New', 'York', '.', 'Please', 'buy', 'me', 'two', 'of', 'them', '.', 'Thanks', '.']
>>> s = "The colour of the wall is blue."
>>> list(
... parser.tokenize(
... 'The colour of the wall is blue.',
... properties={'tokenize.options': 'americanize=true'},
... )
... )
['The', 'color', 'of', 'the', 'wall', 'is', 'blue', '.']
"""
default_properties = {
'annotators': 'tokenize,ssplit',
}
default_properties.update(properties or {})
result = self.api_call(text, properties=default_properties)
for sentence in result['sentences']:
for token in sentence['tokens']:
yield token['originalText'] or token['word']
def tag_sents(self, sentences):
"""
Tag multiple sentences.
Takes multiple sentences as a list where each sentence is a list of
tokens.
:param sentences: Input sentences to tag
:type sentences: list(list(str))
:rtype: list(list(tuple(str, str))
"""
# Converting list(list(str)) -> list(str)
sentences = (' '.join(words) for words in sentences)
return [sentences[0] for sentences in self.raw_tag_sents(sentences)]
def tag(self, sentence):
"""
Tag a list of tokens.
:rtype: list(tuple(str, str))
>>> parser = CoreNLPParser(url='http://localhost:9000', tagtype='ner')
>>> tokens = 'Rami Eid is studying at Stony Brook University in NY'.split()
>>> parser.tag(tokens)
[('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'), ('at', 'O'), ('Stony', 'ORGANIZATION'),
('Brook', 'ORGANIZATION'), ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'O')]
>>> parser = CoreNLPParser(url='http://localhost:9000', tagtype='pos')
>>> tokens = "What is the airspeed of an unladen swallow ?".split()
>>> parser.tag(tokens)
[('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'),
('airspeed', 'NN'), ('of', 'IN'), ('an', 'DT'),
('unladen', 'JJ'), ('swallow', 'VB'), ('?', '.')]
"""
return self.tag_sents([sentence])[0]
def raw_tag_sents(self, sentences):
"""
Tag multiple sentences.
Takes multiple sentences as a list where each sentence is a string.
:param sentences: Input sentences to tag
:type sentences: list(str)
:rtype: list(list(list(tuple(str, str)))
"""
default_properties = {'ssplit.isOneSentence': 'true',
'annotators': 'tokenize,ssplit,' }
# Supports only 'pos' or 'ner' tags.
assert self.tagtype in ['pos', 'ner']
default_properties['annotators'] += self.tagtype
for sentence in sentences:
tagged_data = self.api_call(sentence, properties=default_properties)
yield [[(token['word'], token[self.tagtype]) for token in tagged_sentence['tokens']]
for tagged_sentence in tagged_data['sentences']]
class CoreNLPParser(GenericCoreNLPParser):
"""
>>> parser = CoreNLPParser(url='http://localhost:9000')
>>> next(
... parser.raw_parse('The quick brown fox jumps over the lazy dog.')
... ).pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
_______________|__________________________
| VP |
| _________|___ |
| | PP |
| | ________|___ |
NP | | NP |
____|__________ | | _______|____ |
DT JJ JJ NN VBZ IN DT JJ NN .
| | | | | | | | | |
The quick brown fox jumps over the lazy dog .
>>> (parse_fox, ), (parse_wolf, ) = parser.raw_parse_sents(
... [
... 'The quick brown fox jumps over the lazy dog.',
... 'The quick grey wolf jumps over the lazy fox.',
... ]
... )
>>> parse_fox.pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
_______________|__________________________
| VP |
| _________|___ |
| | PP |
| | ________|___ |
NP | | NP |
____|__________ | | _______|____ |
DT JJ JJ NN VBZ IN DT JJ NN .
| | | | | | | | | |
The quick brown fox jumps over the lazy dog .
>>> parse_wolf.pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
_______________|__________________________
| VP |
| _________|___ |
| | PP |
| | ________|___ |
NP | | NP |
____|_________ | | _______|____ |
DT JJ JJ NN VBZ IN DT JJ NN .
| | | | | | | | | |
The quick grey wolf jumps over the lazy fox .
>>> (parse_dog, ), (parse_friends, ) = parser.parse_sents(
... [
... "I 'm a dog".split(),
... "This is my friends ' cat ( the tabby )".split(),
... ]
... )
>>> parse_dog.pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
_______|____
| VP
| ________|___
NP | NP
| | ___|___
PRP VBP DT NN
| | | |
I 'm a dog
>>> parse_friends.pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
____|___________
| VP
| ___________|_____________
| | NP
| | _______|_________
| | NP PRN
| | _____|_______ ____|______________
NP | NP | | NP |
| | ______|_________ | | ___|____ |
DT VBZ PRP$ NNS POS NN -LRB- DT NN -RRB-
| | | | | | | | | |
This is my friends ' cat -LRB- the tabby -RRB-
>>> parse_john, parse_mary, = parser.parse_text(
... 'John loves Mary. Mary walks.'
... )
>>> parse_john.pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
_____|_____________
| VP |
| ____|___ |
NP | NP |
| | | |
NNP VBZ NNP .
| | | |
John loves Mary .
>>> parse_mary.pretty_print() # doctest: +NORMALIZE_WHITESPACE
ROOT
|
S
_____|____
NP VP |
| | |
NNP VBZ .
| | |
Mary walks .
Special cases
-------------
>>> next(
... parser.raw_parse(
... 'NASIRIYA, Iraq—Iraqi doctors who treated former prisoner of war '
... 'Jessica Lynch have angrily dismissed claims made in her biography '
... 'that she was raped by her Iraqi captors.'
... )
... ).height()
20
>>> next(
... parser.raw_parse(
... "The broader Standard & Poor's 500 Index <.SPX> was 0.46 points lower, or "
... '0.05 percent, at 997.02.'
... )
... ).height()
9
"""
_OUTPUT_FORMAT = 'penn'
parser_annotator = 'parse'
def make_tree(self, result):
return Tree.fromstring(result['parse'])
class CoreNLPDependencyParser(GenericCoreNLPParser):
"""Dependency parser.
>>> dep_parser = CoreNLPDependencyParser(url='http://localhost:9000')
>>> parse, = dep_parser.raw_parse(
... 'The quick brown fox jumps over the lazy dog.'
... )
>>> print(parse.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
The DT 4 det
quick JJ 4 amod
brown JJ 4 amod
fox NN 5 nsubj
jumps VBZ 0 ROOT
over IN 9 case
the DT 9 det
lazy JJ 9 amod
dog NN 5 nmod
. . 5 punct
>>> print(parse.tree()) # doctest: +NORMALIZE_WHITESPACE
(jumps (fox The quick brown) (dog over the lazy) .)
>>> for governor, dep, dependent in parse.triples():
... print(governor, dep, dependent) # doctest: +NORMALIZE_WHITESPACE
('jumps', 'VBZ') nsubj ('fox', 'NN')
('fox', 'NN') det ('The', 'DT')
('fox', 'NN') amod ('quick', 'JJ')
('fox', 'NN') amod ('brown', 'JJ')
('jumps', 'VBZ') nmod ('dog', 'NN')
('dog', 'NN') case ('over', 'IN')
('dog', 'NN') det ('the', 'DT')
('dog', 'NN') amod ('lazy', 'JJ')
('jumps', 'VBZ') punct ('.', '.')
>>> (parse_fox, ), (parse_dog, ) = dep_parser.raw_parse_sents(
... [
... 'The quick brown fox jumps over the lazy dog.',
... 'The quick grey wolf jumps over the lazy fox.',
... ]
... )
>>> print(parse_fox.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
The DT 4 det
quick JJ 4 amod
brown JJ 4 amod
fox NN 5 nsubj
jumps VBZ 0 ROOT
over IN 9 case
the DT 9 det
lazy JJ 9 amod
dog NN 5 nmod
. . 5 punct
>>> print(parse_dog.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
The DT 4 det
quick JJ 4 amod
grey JJ 4 amod
wolf NN 5 nsubj
jumps VBZ 0 ROOT
over IN 9 case
the DT 9 det
lazy JJ 9 amod
fox NN 5 nmod
. . 5 punct
>>> (parse_dog, ), (parse_friends, ) = dep_parser.parse_sents(
... [
... "I 'm a dog".split(),
... "This is my friends ' cat ( the tabby )".split(),
... ]
... )
>>> print(parse_dog.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
I PRP 4 nsubj
'm VBP 4 cop
a DT 4 det
dog NN 0 ROOT
>>> print(parse_friends.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
This DT 6 nsubj
is VBZ 6 cop
my PRP$ 4 nmod:poss
friends NNS 6 nmod:poss
' POS 4 case
cat NN 0 ROOT
-LRB- -LRB- 9 punct
the DT 9 det
tabby NN 6 appos
-RRB- -RRB- 9 punct
>>> parse_john, parse_mary, = dep_parser.parse_text(
... 'John loves Mary. Mary walks.'
... )
>>> print(parse_john.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
John NNP 2 nsubj
loves VBZ 0 ROOT
Mary NNP 2 dobj
. . 2 punct
>>> print(parse_mary.to_conll(4)) # doctest: +NORMALIZE_WHITESPACE
Mary NNP 2 nsubj
walks VBZ 0 ROOT
. . 2 punct
Special cases
-------------
Non-breaking space inside of a token.
>>> len(
... next(
... dep_parser.raw_parse(
... 'Anhalt said children typically treat a 20-ounce soda bottle as one '
... 'serving, while it actually contains 2 1/2 servings.'
... )
... ).nodes
... )
21
Phone numbers.
>>> len(
... next(
... dep_parser.raw_parse('This is not going to crash: 01 111 555.')
... ).nodes
... )
10
>>> print(
... next(
... dep_parser.raw_parse('The underscore _ should not simply disappear.')
... ).to_conll(4)
... ) # doctest: +NORMALIZE_WHITESPACE
The DT 3 det
underscore VBP 3 amod
_ NN 7 nsubj
should MD 7 aux
not RB 7 neg
simply RB 7 advmod
disappear VB 0 ROOT
. . 7 punct
>>> print(
... '\\n'.join(
... next(
... dep_parser.raw_parse(
... 'for all of its insights into the dream world of teen life , and its electronic expression through '
... 'cyber culture , the film gives no quarter to anyone seeking to pull a cohesive story out of its 2 '
... '1/2-hour running time .'
... )
... ).to_conll(4).split('\\n')[-8:]
... )
... )
its PRP$ 40 nmod:poss
2 1/2 CD 40 nummod
- : 40 punct
hour NN 31 nmod
running VBG 42 amod
time NN 40 dep
. . 24 punct
<BLANKLINE>
"""
_OUTPUT_FORMAT = 'conll2007'
parser_annotator = 'depparse'
def make_tree(self, result):
return DependencyGraph(
(
' '.join(n_items[1:]) # NLTK expects an iterable of strings...
for n_items in sorted(transform(result))
),
cell_separator=' ', # To make sure that a non-breaking space is kept inside of a token.
)
def transform(sentence):
for dependency in sentence['basicDependencies']:
dependent_index = dependency['dependent']
token = sentence['tokens'][dependent_index - 1]
# Return values that we don't know as '_'. Also, consider tag and ctag
# to be equal.
yield (
dependent_index,
'_',
token['word'],
token['lemma'],
token['pos'],
token['pos'],
'_',
str(dependency['governor']),
dependency['dep'],
'_',
'_',
)
def setup_module(module):
from nose import SkipTest
raise SkipTest('Skipping all CoreNLP tests.')
global server
try:
server = CoreNLPServer(port=9000)
except LookupError as e:
raise SkipTest('Could not instantiate CoreNLPServer.')
try:
server.start()
except CoreNLPServerError as e:
raise SkipTest(
'Skipping CoreNLP tests because the server could not be started. '
'Make sure that the 9000 port is free. '
'{}'.format(e.strerror)
)
def teardown_module(module):
return
server.stop()
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