mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
from typing import Optional, Any, Dict
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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 .lex_attrs import LEX_ATTRS
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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 ...scorer import Scorer
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from ...symbols import POS
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from ...training import validate_examples
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from ...util import DummyTokenizer, registry, load_config_from_str
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DEFAULT_CONFIG = """
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[nlp]
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[nlp.tokenizer]
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@tokenizers = "spacy.ko.KoreanTokenizer"
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"""
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@registry.tokenizers("spacy.ko.KoreanTokenizer")
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def create_tokenizer():
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def korean_tokenizer_factory(nlp):
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return KoreanTokenizer(nlp)
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return korean_tokenizer_factory
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class KoreanTokenizer(DummyTokenizer):
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def __init__(self, nlp: Optional[Language] = None):
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self.vocab = nlp.vocab
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MeCab = try_mecab_import()
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self.mecab_tokenizer = MeCab("-F%f[0],%f[7]")
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def __del__(self):
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self.mecab_tokenizer.__del__()
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def __call__(self, text: str) -> Doc:
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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.pos = TAG_MAP[token.tag_][POS]
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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: str) -> Dict[str, Any]:
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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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for node in self.mecab_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 {"surface": surface, "lemma": lemma, "tag": tag}
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def score(self, examples):
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validate_examples(examples, "KoreanTokenizer.score")
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return Scorer.score_tokenization(examples)
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class KoreanDefaults(Language.Defaults):
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config = load_config_from_str(DEFAULT_CONFIG)
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lex_attr_getters = LEX_ATTRS
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stop_words = STOP_WORDS
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writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
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class Korean(Language):
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lang = "ko"
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Defaults = KoreanDefaults
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def try_mecab_import() -> None:
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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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) from None
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def check_spaces(text, tokens):
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prev_end = -1
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start = 0
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for token in tokens:
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idx = text.find(token, start)
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if prev_end > 0:
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yield prev_end != idx
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prev_end = idx + len(token)
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start = prev_end
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if start > 0:
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yield False
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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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