mirror of
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119 lines
3.6 KiB
Python
119 lines
3.6 KiB
Python
# encoding: utf8
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from __future__ import unicode_literals, print_function
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import re
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from collections import namedtuple
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from .stop_words import STOP_WORDS
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from .tag_map import TAG_MAP
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from ...attrs import LANG
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from ...language import Language
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from ...tokens import Doc, Token
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from ...compat import copy_reg
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from ...util import DummyTokenizer
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ShortUnitWord = namedtuple("ShortUnitWord", ["surface", "lemma", "pos"])
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# TODO: Is this the right place for this?
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Token.set_extension("mecab_tag", default=None)
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def try_mecab_import():
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"""Mecab is required for Japanese support, so check for it.
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It it's not available blow up and explain how to fix it."""
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try:
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import MeCab
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return MeCab
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except ImportError:
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raise ImportError(
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"Japanese support requires MeCab: "
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"https://github.com/SamuraiT/mecab-python3"
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)
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def resolve_pos(token):
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"""If necessary, add a field to the POS tag for UD mapping.
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Under Universal Dependencies, sometimes the same Unidic POS tag can
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be mapped differently depending on the literal token or its context
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in the sentence. This function adds information to the POS tag to
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resolve ambiguous mappings.
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"""
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# TODO: This is a first take. The rules here are crude approximations.
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# For many of these, full dependencies are needed to properly resolve
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# PoS mappings.
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if token.pos == "連体詞,*,*,*":
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if re.match(r"[こそあど此其彼]の", token.surface):
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return token.pos + ",DET"
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if re.match(r"[こそあど此其彼]", token.surface):
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return token.pos + ",PRON"
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return token.pos + ",ADJ"
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return token.pos
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def detailed_tokens(tokenizer, text):
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"""Format Mecab output into a nice data structure, based on Janome."""
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node = tokenizer.parseToNode(text)
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node = node.next # first node is beginning of sentence and empty, skip it
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words = []
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while node.posid != 0:
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surface = node.surface
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base = surface # a default value. Updated if available later.
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parts = node.feature.split(",")
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pos = ",".join(parts[0:4])
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if len(parts) > 7:
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# this information is only available for words in the tokenizer
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# dictionary
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base = parts[7]
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words.append(ShortUnitWord(surface, base, pos))
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node = node.next
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return words
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class JapaneseTokenizer(DummyTokenizer):
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def __init__(self, cls, nlp=None):
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self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
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self.tokenizer = try_mecab_import().Tagger()
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self.tokenizer.parseToNode("") # see #2901
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def __call__(self, text):
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dtokens = detailed_tokens(self.tokenizer, text)
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words = [x.surface for x in dtokens]
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spaces = [False] * len(words)
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doc = Doc(self.vocab, words=words, spaces=spaces)
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for token, dtoken in zip(doc, dtokens):
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token._.mecab_tag = dtoken.pos
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token.tag_ = resolve_pos(dtoken)
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token.lemma_ = dtoken.lemma
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return doc
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class JapaneseDefaults(Language.Defaults):
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lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
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lex_attr_getters[LANG] = lambda _text: "ja"
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stop_words = STOP_WORDS
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tag_map = TAG_MAP
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@classmethod
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def create_tokenizer(cls, nlp=None):
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return JapaneseTokenizer(cls, nlp)
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class Japanese(Language):
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lang = "ja"
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Defaults = JapaneseDefaults
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def make_doc(self, text):
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return self.tokenizer(text)
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def pickle_japanese(instance):
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return Japanese, tuple()
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copy_reg.pickle(Japanese, pickle_japanese)
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__all__ = ["Japanese"]
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