mirror of https://github.com/explosion/spaCy.git
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
from __future__ import unicode_literals
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from os import path
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from .. import orth
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from ..vocab import Vocab
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from ..tokenizer import Tokenizer
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from ..syntax.parser import GreedyParser
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from ..tokens import Tokens
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from .pos import EnPosTagger
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from .pos import POS_TAGS
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from .attrs import get_flags
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def get_lex_props(string):
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return {'flags': get_flags(string), 'dense': 1}
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LOCAL_DATA_DIR = path.join(path.dirname(__file__), 'data')
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class English(object):
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"""The English NLP pipeline.
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Provides a tokenizer, lexicon, part-of-speech tagger and parser.
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Keyword args:
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data_dir (unicode): A path to a directory, from which to load the pipeline.
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If None, looks for a directory named "data/" in the same directory as
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the present file, i.e. path.join(path.dirname(__file__, 'data')).
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If path.join(data_dir, 'pos') exists, the tagger is loaded from it.
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If path.join(data_dir, 'deps') exists, the parser is loaded from it.
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See Pipeline Directory Structure for details.
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Attributes:
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vocab (spacy.vocab.Vocab): The lexicon.
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strings (spacy.strings.StringStore): Encode/decode strings to/from integer IDs.
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tokenizer (spacy.tokenizer.Tokenizer): The start of the pipeline.
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tagger (spacy.en.pos.EnPosTagger):
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The part-of-speech tagger, which also performs lemmatization and
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morphological analysis.
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parser (spacy.syntax.parser.GreedyParser):
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A greedy shift-reduce dependency parser.
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"""
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def __init__(self, data_dir=LOCAL_DATA_DIR):
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self._data_dir = data_dir
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self.vocab = Vocab(data_dir=path.join(data_dir, 'vocab'),
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get_lex_props=get_lex_props)
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tag_names = list(POS_TAGS.keys())
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tag_names.sort()
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if data_dir is None:
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self.tokenizer = Tokenizer(self.vocab, {}, None, None, None,
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POS_TAGS, tag_names)
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else:
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self.tokenizer = Tokenizer.from_dir(self.vocab, path.join(data_dir, 'tokenizer'),
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POS_TAGS, tag_names)
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self.strings = self.vocab.strings
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self._tagger = None
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self._parser = None
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@property
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def tagger(self):
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if self._tagger is None:
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self._tagger = EnPosTagger(self.vocab.strings, self._data_dir)
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return self._tagger
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@property
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def parser(self):
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if self._parser is None:
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self._parser = GreedyParser(path.join(self._data_dir, 'deps'))
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return self._parser
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def __call__(self, text, tag=True, parse=False):
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"""Apply the pipeline to some text.
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Args:
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text (unicode): The text to be processed.
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Keyword args:
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tag (bool): Whether to add part-of-speech tags to the text. This
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will also set morphological analysis and lemmas.
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parse (bool): Whether to add dependency-heads and labels to the text.
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Returns:
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tokens (spacy.tokens.Tokens):
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"""
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tokens = self.tokenizer.tokenize(text)
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if tag:
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self.tagger(tokens)
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if parse:
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self.parser.parse(tokens)
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return tokens
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@property
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def tags(self):
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"""List of part-of-speech tag names."""
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return self.tagger.tag_names
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