from __future__ import unicode_literals from os import path import re from .. import orth from ..vocab import Vocab from ..tokenizer import Tokenizer from ..syntax.parser import GreedyParser from ..syntax.arc_eager import ArcEager from ..syntax.ner import BiluoPushDown from ..tokens import Tokens from ..multi_words import RegexMerger from .pos import EnPosTagger from .pos import POS_TAGS from .attrs import get_flags from . import regexes from ..util import read_lang_data def get_lex_props(string): return { 'flags': get_flags(string), 'length': len(string), 'orth': string, 'lower': string.lower(), 'norm': string, 'shape': orth.word_shape(string), 'prefix': string[0], 'suffix': string[-3:], 'cluster': 0, 'prob': 0, 'sentiment': 0 } LOCAL_DATA_DIR = path.join(path.dirname(__file__), 'data') parse_if_model_present = -1 class English(object): """The English NLP pipeline. Provides a tokenizer, lexicon, part-of-speech tagger and parser. Keyword args: data_dir (unicode): A path to a directory, from which to load the pipeline. By default, data is installed within the spaCy package directory. So if no data_dir is specified, spaCy attempts to load from a directory named "data" that is a sibling of the spacy/en/__init__.py file. You can find the location of this file by running: $ python -c "import spacy.en; print spacy.en.__file__" To prevent any data files from being loaded, pass data_dir=None. This is useful if you want to construct a lexicon, which you'll then save for later loading. """ ParserTransitionSystem = ArcEager EntityTransitionSystem = BiluoPushDown def __init__(self, data_dir=''): if data_dir == '': data_dir = LOCAL_DATA_DIR self._data_dir = data_dir self.vocab = Vocab(data_dir=path.join(data_dir, 'vocab') if data_dir else None, get_lex_props=get_lex_props) tag_names = list(POS_TAGS.keys()) tag_names.sort() if data_dir is None: tok_rules = {} prefix_re = None suffix_re = None infix_re = None self.has_parser_model = False self.has_tagger_model = False self.has_entity_model = False else: tok_data_dir = path.join(data_dir, 'tokenizer') tok_rules, prefix_re, suffix_re, infix_re = read_lang_data(tok_data_dir) prefix_re = re.compile(prefix_re) suffix_re = re.compile(suffix_re) infix_re = re.compile(infix_re) self.has_parser_model = path.exists(path.join(self._data_dir, 'deps')) self.has_tagger_model = path.exists(path.join(self._data_dir, 'pos')) self.has_entity_model = path.exists(path.join(self._data_dir, 'ner')) self.tokenizer = Tokenizer(self.vocab, tok_rules, prefix_re, suffix_re, infix_re, POS_TAGS, tag_names) self.mwe_merger = RegexMerger([ ('IN', 'O', regexes.MW_PREPOSITIONS_RE), ('CD', 'TIME', regexes.TIME_RE), ('NNP', 'DATE', regexes.DAYS_RE), ('CD', 'MONEY', regexes.MONEY_RE)]) # These are lazy-loaded self._tagger = None self._parser = None self._entity = None @property def tagger(self): if self._tagger is None: self._tagger = EnPosTagger(self.vocab.strings, self._data_dir) return self._tagger @property def parser(self): if self._parser is None: self._parser = GreedyParser(self.vocab.strings, path.join(self._data_dir, 'deps'), self.ParserTransitionSystem) return self._parser @property def entity(self): if self._entity is None: self._entity = GreedyParser(self.vocab.strings, path.join(self._data_dir, 'ner'), self.EntityTransitionSystem) return self._entity def __call__(self, text, tag=True, parse=parse_if_model_present, entity=parse_if_model_present, merge_mwes=True): """Apply the pipeline to some text. The text can span multiple sentences, and can contain arbtrary whitespace. Alignment into the original string The tagger and parser are lazy-loaded the first time they are required. Loading the parser model usually takes 5-10 seconds. Args: text (unicode): The text to be processed. Keyword args: tag (bool): Whether to add part-of-speech tags to the text. Also sets morphological analysis and lemmas. parse (True, False, -1): Whether to add labelled syntactic dependencies. -1 (default) is "guess": It will guess True if tag=True and the model has been installed. Returns: tokens (spacy.tokens.Tokens): >>> from spacy.en import English >>> nlp = English() >>> tokens = nlp('An example sentence. Another example sentence.') >>> tokens[0].orth_, tokens[0].head.tag_ ('An', 'NN') """ if parse == True and tag == False: msg = ("Incompatible arguments: tag=False, parse=True" "Part-of-speech tags are required for parsing.") raise ValueError(msg) if entity == True and tag == False: msg = ("Incompatible arguments: tag=False, entity=True" "Part-of-speech tags are required for entity recognition.") raise ValueError(msg) tokens = self.tokenizer(text) if parse == -1 and tag == False: parse = False elif parse == -1 and not self.has_parser_model: parse = False if entity == -1 and tag == False: entity = False elif entity == -1 and not self.has_entity_model: entity = False if tag and self.has_tagger_model: self.tagger(tokens) if parse == True and not self.has_parser_model: msg = ("Received parse=True, but parser model not found.\n\n" "Run:\n" "$ python -m spacy.en.download\n" "To install the model.") raise IOError(msg) if entity == True and not self.has_entity_model: msg = ("Received entity=True, but entity model not found.\n\n" "Run:\n" "$ python -m spacy.en.download\n" "To install the model.") raise IOError(msg) if parse and self.has_parser_model: self.parser(tokens) if entity and self.has_entity_model: self.entity(tokens) if merge_mwes and self.mwe_merger is not None: self.mwe_merger(tokens) return tokens @property def tags(self): """List of part-of-speech tag names.""" return self.tagger.tag_names