# Natural Language Toolkit: Interface to the CoreNLP REST API. # # Copyright (C) 2001-2024 NLTK Project # Author: Dmitrijs Milajevs # # URL: # For license information, see LICENSE.TXT import json import os # required for doctests import re import socket import time from typing import List, Tuple from nltk.internals import _java_options, config_java, find_jar_iter, java from nltk.parse.api import ParserI from nltk.parse.dependencygraph import DependencyGraph from nltk.tag.api import TaggerI from nltk.tokenize.api import TokenizerI from nltk.tree import Tree _stanford_url = "https://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: _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 OSError: port = try_port() corenlp_options.extend(["-port", str(port)]) else: try_port(port) corenlp_options.extend(["-port", str(port)]) self.url = f"http://localhost:{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, stdout="devnull", stderr="devnull"): """Starts the CoreNLP server :param stdout, stderr: Specifies where CoreNLP output is redirected. Valid values are 'devnull', 'stdout', 'pipe' """ 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: self.popen = java( cmd, classpath=self._classpath, blocking=False, stdout=stdout, stderr=stderr, ) 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, strict_json=True, ): 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.strict_json = strict_json 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, timeout=60): 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), headers={"Content-Type": f"text/plain; charset={self.encoding}"}, timeout=timeout, ) response.raise_for_status() return response.json(strict=self.strict_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.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. Skip these tests if CoreNLP is likely not ready. >>> from nltk.test.setup_fixt import check_jar >>> check_jar(CoreNLPServer._JAR, env_vars=("CORENLP",), is_regex=True) The CoreNLP server can be started using the following notation, although we recommend the `with CoreNLPServer() as server:` context manager notation to ensure that the server is always stopped. >>> server = CoreNLPServer() >>> server.start() >>> parser = CoreNLPParser(url=server.url) >>> 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', 'colour', 'of', 'the', 'wall', 'is', 'blue', '.'] >>> server.stop() """ 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: str) -> List[Tuple[str, str]]: """ Tag a list of tokens. :rtype: list(tuple(str, str)) Skip these tests if CoreNLP is likely not ready. >>> from nltk.test.setup_fixt import check_jar >>> check_jar(CoreNLPServer._JAR, env_vars=("CORENLP",), is_regex=True) The CoreNLP server can be started using the following notation, although we recommend the `with CoreNLPServer() as server:` context manager notation to ensure that the server is always stopped. >>> server = CoreNLPServer() >>> server.start() >>> parser = CoreNLPParser(url=server.url, tagtype='ner') >>> tokens = 'Rami Eid is studying at Stony Brook University in NY'.split() >>> parser.tag(tokens) # doctest: +NORMALIZE_WHITESPACE [('Rami', 'PERSON'), ('Eid', 'PERSON'), ('is', 'O'), ('studying', 'O'), ('at', 'O'), ('Stony', 'ORGANIZATION'), ('Brook', 'ORGANIZATION'), ('University', 'ORGANIZATION'), ('in', 'O'), ('NY', 'STATE_OR_PROVINCE')] >>> parser = CoreNLPParser(url=server.url, tagtype='pos') >>> tokens = "What is the airspeed of an unladen swallow ?".split() >>> parser.tag(tokens) # doctest: +NORMALIZE_WHITESPACE [('What', 'WP'), ('is', 'VBZ'), ('the', 'DT'), ('airspeed', 'NN'), ('of', 'IN'), ('an', 'DT'), ('unladen', 'JJ'), ('swallow', 'VB'), ('?', '.')] >>> server.stop() """ 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): """ Skip these tests if CoreNLP is likely not ready. >>> from nltk.test.setup_fixt import check_jar >>> check_jar(CoreNLPServer._JAR, env_vars=("CORENLP",), is_regex=True) The recommended usage of `CoreNLPParser` is using the context manager notation: >>> with CoreNLPServer() as server: ... parser = CoreNLPParser(url=server.url) ... 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 . Alternatively, the server can be started using the following notation. Note that `CoreNLPServer` does not need to be used if the CoreNLP server is started outside of Python. >>> server = CoreNLPServer() >>> server.start() >>> parser = CoreNLPParser(url=server.url) >>> (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 | | | | | _____|_______ | | | 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() 14 >>> 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() 11 >>> server.stop() """ _OUTPUT_FORMAT = "penn" parser_annotator = "parse" def make_tree(self, result): return Tree.fromstring(result["parse"]) class CoreNLPDependencyParser(GenericCoreNLPParser): """Dependency parser. Skip these tests if CoreNLP is likely not ready. >>> from nltk.test.setup_fixt import check_jar >>> check_jar(CoreNLPServer._JAR, env_vars=("CORENLP",), is_regex=True) The recommended usage of `CoreNLPParser` is using the context manager notation: >>> with CoreNLPServer() as server: ... dep_parser = CoreNLPDependencyParser(url=server.url) ... 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 obl . . 5 punct Alternatively, the server can be started using the following notation. Note that `CoreNLPServer` does not need to be used if the CoreNLP server is started outside of Python. >>> server = CoreNLPServer() >>> server.start() >>> dep_parser = CoreNLPDependencyParser(url=server.url) >>> parse, = dep_parser.raw_parse('The quick brown fox jumps over the lazy dog.') >>> 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') obl ('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 obl . . 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 obl . . 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- 9 punct the DT 9 det tabby NN 6 dep ) -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 obj . . 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 ... ) 23 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 2 det underscore NN 7 nsubj _ NFP 7 punct should MD 7 aux not RB 7 advmod simply RB 7 advmod disappear VB 0 ROOT . . 7 punct >>> print( ... 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) ... ) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS for IN 2 case all DT 24 obl of IN 5 case its PRP$ 5 nmod:poss insights NNS 2 nmod into IN 9 case the DT 9 det dream NN 9 compound world NN 5 nmod of IN 12 case teen NN 12 compound ... >>> server.stop() """ _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"], "_", "_", )