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https://github.com/explosion/spaCy.git
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58f06e6180
* start lang/ko * add test codes * using natto-py * add test_ko_tokenizer_full_tags() * spaCy contributor agreement * external dependency for ko * collections.namedtuple for python version < 3.5 * case fix * tuple unpacking * add jongseong(final consonant) * apply mecab option * Remove Pipfile for now Co-authored-by: Ines Montani <ines@ines.io>
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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import sys
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from .stop_words import STOP_WORDS
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from .tag_map import TAG_MAP, POS
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from ...attrs import LANG
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from ...language import Language
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from ...tokens import Doc
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from ...compat import copy_reg
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from ...util import DummyTokenizer
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from ...compat import is_python3, is_python_pre_3_5
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is_python_post_3_7 = is_python3 and sys.version_info[1] >= 7
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# fmt: off
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if is_python_pre_3_5:
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from collections import namedtuple
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Morpheme = namedtuple("Morpheme", "surface lemma tag")
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elif is_python_post_3_7:
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class Morpheme:
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surface: str
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lemma: str
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tag: str
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else:
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from typing import NamedTuple
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class Morpheme(NamedTuple):
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surface: str
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lemma: str
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tag: str
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def try_mecab_import():
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try:
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from natto import MeCab
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return MeCab
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except ImportError:
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raise ImportError(
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"Korean support requires [mecab-ko](https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md), "
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"[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
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"and [natto-py](https://github.com/buruzaemon/natto-py)"
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)
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# fmt: on
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def check_spaces(text, tokens):
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token_pattern = re.compile(r"\s?".join(f"({t})" for t in tokens))
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m = token_pattern.match(text)
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if m is not None:
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for i in range(1, m.lastindex):
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yield m.end(i) < m.start(i + 1)
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yield False
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class KoreanTokenizer(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()
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def __call__(self, text):
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dtokens = list(self.detailed_tokens(text))
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surfaces = [dt.surface for dt in dtokens]
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doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
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for token, dtoken in zip(doc, dtokens):
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first_tag, sep, eomi_tags = dtoken.tag.partition("+")
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token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
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token.lemma_ = dtoken.lemma
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doc.user_data["full_tags"] = [dt.tag for dt in dtokens]
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return doc
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def detailed_tokens(self, text):
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# 품사 태그(POS)[0], 의미 부류(semantic class)[1], 종성 유무(jongseong)[2], 읽기(reading)[3],
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# 타입(type)[4], 첫번째 품사(start pos)[5], 마지막 품사(end pos)[6], 표현(expression)[7], *
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with self.Tokenizer("-F%f[0],%f[7]") as tokenizer:
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for node in tokenizer.parse(text, as_nodes=True):
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if node.is_eos():
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break
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surface = node.surface
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feature = node.feature
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tag, _, expr = feature.partition(",")
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lemma, _, remainder = expr.partition("/")
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if lemma == "*":
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lemma = surface
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yield Morpheme(surface, lemma, tag)
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class KoreanDefaults(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: "ko"
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stop_words = STOP_WORDS
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tag_map = TAG_MAP
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writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
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@classmethod
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def create_tokenizer(cls, nlp=None):
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return KoreanTokenizer(cls, nlp)
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class Korean(Language):
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lang = "ko"
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Defaults = KoreanDefaults
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def make_doc(self, text):
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return self.tokenizer(text)
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def pickle_korean(instance):
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return Korean, tuple()
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copy_reg.pickle(Korean, pickle_korean)
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__all__ = ["Korean"]
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