mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
from __future__ import unicode_literals
|
|
from os import path
|
|
|
|
from .. import orth
|
|
from ..vocab import Vocab
|
|
from ..tokenizer import Tokenizer
|
|
from ..syntax.parser import GreedyParser
|
|
from ..tokens import Tokens
|
|
from .pos import EnPosTagger
|
|
from .pos import POS_TAGS
|
|
from .attrs import get_flags
|
|
|
|
|
|
DATA_DIR = path.join(path.dirname(__file__), 'data')
|
|
|
|
|
|
def get_lex_props(string):
|
|
return {'flags': get_flags(string), 'dense': 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.
|
|
If None, looks for a directory named "data/" in the same directory as
|
|
the present file, i.e. path.join(path.dirname(__file__, 'data')).
|
|
If path.join(data_dir, 'pos') exists, the tagger is loaded from it.
|
|
If path.join(data_dir, 'deps') exists, the parser is loaded from it.
|
|
See Pipeline Directory Structure for details.
|
|
|
|
Attributes:
|
|
vocab (spacy.vocab.Vocab): The lexicon.
|
|
|
|
strings (spacy.strings.StringStore): Encode/decode strings to/from integer IDs.
|
|
|
|
tokenizer (spacy.tokenizer.Tokenizer): The start of the pipeline.
|
|
|
|
tagger (spacy.en.pos.EnPosTagger):
|
|
The part-of-speech tagger, which also performs lemmatization and
|
|
morphological analysis.
|
|
|
|
parser (spacy.syntax.parser.GreedyParser):
|
|
A greedy shift-reduce dependency parser.
|
|
"""
|
|
def __init__(self, data_dir=None):
|
|
if data_dir is None:
|
|
data_dir = path.join(path.dirname(__file__), 'data')
|
|
self._data_dir = data_dir
|
|
self.vocab = Vocab(data_dir=path.join(data_dir, 'vocab'),
|
|
get_lex_props=get_lex_props)
|
|
tag_names = list(POS_TAGS.keys())
|
|
tag_names.sort()
|
|
self.tokenizer = Tokenizer.from_dir(self.vocab, path.join(data_dir, 'tokenizer'),
|
|
POS_TAGS, tag_names)
|
|
self.strings = self.vocab.strings
|
|
self._tagger = None
|
|
self._parser = 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(path.join(self._data_dir, 'deps'))
|
|
return self._parser
|
|
|
|
def __call__(self, text, tag=True, parse=False):
|
|
"""Apply the pipeline to some text.
|
|
|
|
Args:
|
|
text (unicode): The text to be processed.
|
|
|
|
Keyword args:
|
|
tag (bool): Whether to add part-of-speech tags to the text. This
|
|
will also set morphological analysis and lemmas.
|
|
|
|
parse (bool): Whether to add dependency-heads and labels to the text.
|
|
|
|
Returns:
|
|
tokens (spacy.tokens.Tokens):
|
|
"""
|
|
tokens = self.tokenizer.tokenize(text)
|
|
if tag:
|
|
self.tagger(tokens)
|
|
if parse:
|
|
self.parser.parse(tokens)
|
|
return tokens
|
|
|
|
@property
|
|
def tags(self):
|
|
"""List of part-of-speech tag names."""
|
|
return self.tagger.tag_names
|