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126 lines
4.2 KiB
126 lines
4.2 KiB
from typing import Any, Dict, Iterator
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from ...language import BaseDefaults, Language
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from ...scorer import Scorer
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from ...symbols import POS, X
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from ...tokens import Doc
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from ...training import validate_examples
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from ...util import DummyTokenizer, load_config_from_str, registry
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from ...vocab import Vocab
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from .lex_attrs import LEX_ATTRS
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from .punctuation import TOKENIZER_INFIXES
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from .stop_words import STOP_WORDS
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from .tag_map import TAG_MAP
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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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def create_tokenizer():
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def korean_tokenizer_factory(nlp):
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return KoreanTokenizer(nlp.vocab)
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return korean_tokenizer_factory
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class KoreanTokenizer(DummyTokenizer):
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def __init__(self, vocab: Vocab):
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self.vocab = vocab
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self._mecab = try_mecab_import() # type: ignore[func-returns-value]
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self._mecab_tokenizer = None
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@property
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def mecab_tokenizer(self):
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# This is a property so that initializing a pipeline with blank:ko is
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# possible without actually requiring mecab-ko, e.g. to run
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# `spacy init vectors ko` for a pipeline that will have a different
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# tokenizer in the end. The languages need to match for the vectors
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# to be imported and there's no way to pass a custom config to
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# `init vectors`.
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if self._mecab_tokenizer is None:
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self._mecab_tokenizer = self._mecab("-F%f[0],%f[7]")
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return self._mecab_tokenizer
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def __reduce__(self):
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return KoreanTokenizer, (self.vocab,)
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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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if token.tag_ in TAG_MAP:
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token.pos = TAG_MAP[token.tag_][POS]
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else:
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token.pos = X
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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) -> Iterator[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(BaseDefaults):
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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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infixes = TOKENIZER_INFIXES
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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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'The Korean tokenizer ("spacy.ko.KoreanTokenizer") requires '
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"[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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__all__ = ["Korean"]
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