spaCy/spacy/lang/ko/__init__.py
Daniël de Kok e2b70df012
Configure isort to use the Black profile, recursively isort the spacy module (#12721)
* Use isort with Black profile

* isort all the things

* Fix import cycles as a result of import sorting

* Add DOCBIN_ALL_ATTRS type definition

* Add isort to requirements

* Remove isort from build dependencies check

* Typo
2023-06-14 17:48:41 +02:00

127 lines
4.2 KiB
Python

from typing import Any, Dict, Iterator
from ...language import BaseDefaults, Language
from ...scorer import Scorer
from ...symbols import POS, X
from ...tokens import Doc
from ...training import validate_examples
from ...util import DummyTokenizer, load_config_from_str, registry
from ...vocab import Vocab
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
DEFAULT_CONFIG = """
[nlp]
[nlp.tokenizer]
@tokenizers = "spacy.ko.KoreanTokenizer"
"""
@registry.tokenizers("spacy.ko.KoreanTokenizer")
def create_tokenizer():
def korean_tokenizer_factory(nlp):
return KoreanTokenizer(nlp.vocab)
return korean_tokenizer_factory
class KoreanTokenizer(DummyTokenizer):
def __init__(self, vocab: Vocab):
self.vocab = vocab
self._mecab = try_mecab_import() # type: ignore[func-returns-value]
self._mecab_tokenizer = None
@property
def mecab_tokenizer(self):
# This is a property so that initializing a pipeline with blank:ko is
# possible without actually requiring mecab-ko, e.g. to run
# `spacy init vectors ko` for a pipeline that will have a different
# tokenizer in the end. The languages need to match for the vectors
# to be imported and there's no way to pass a custom config to
# `init vectors`.
if self._mecab_tokenizer is None:
self._mecab_tokenizer = self._mecab("-F%f[0],%f[7]")
return self._mecab_tokenizer
def __reduce__(self):
return KoreanTokenizer, (self.vocab,)
def __call__(self, text: str) -> Doc:
dtokens = list(self.detailed_tokens(text))
surfaces = [dt["surface"] for dt in dtokens]
doc = Doc(self.vocab, words=surfaces, spaces=list(check_spaces(text, surfaces)))
for token, dtoken in zip(doc, dtokens):
first_tag, sep, eomi_tags = dtoken["tag"].partition("+")
token.tag_ = first_tag # stem(어간) or pre-final(선어말 어미)
if token.tag_ in TAG_MAP:
token.pos = TAG_MAP[token.tag_][POS]
else:
token.pos = X
token.lemma_ = dtoken["lemma"]
doc.user_data["full_tags"] = [dt["tag"] for dt in dtokens]
return doc
def detailed_tokens(self, text: str) -> Iterator[Dict[str, Any]]:
# 품사 태그(POS)[0], 의미 부류(semantic class)[1], 종성 유무(jongseong)[2], 읽기(reading)[3],
# 타입(type)[4], 첫번째 품사(start pos)[5], 마지막 품사(end pos)[6], 표현(expression)[7], *
for node in self.mecab_tokenizer.parse(text, as_nodes=True):
if node.is_eos():
break
surface = node.surface
feature = node.feature
tag, _, expr = feature.partition(",")
lemma, _, remainder = expr.partition("/")
if lemma == "*":
lemma = surface
yield {"surface": surface, "lemma": lemma, "tag": tag}
def score(self, examples):
validate_examples(examples, "KoreanTokenizer.score")
return Scorer.score_tokenization(examples)
class KoreanDefaults(BaseDefaults):
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
infixes = TOKENIZER_INFIXES
class Korean(Language):
lang = "ko"
Defaults = KoreanDefaults
def try_mecab_import() -> None:
try:
from natto import MeCab
return MeCab
except ImportError:
raise ImportError(
'The Korean tokenizer ("spacy.ko.KoreanTokenizer") requires '
"[mecab-ko](https://bitbucket.org/eunjeon/mecab-ko/src/master/README.md), "
"[mecab-ko-dic](https://bitbucket.org/eunjeon/mecab-ko-dic), "
"and [natto-py](https://github.com/buruzaemon/natto-py)"
) from None
def check_spaces(text, tokens):
prev_end = -1
start = 0
for token in tokens:
idx = text.find(token, start)
if prev_end > 0:
yield prev_end != idx
prev_end = idx + len(token)
start = prev_end
if start > 0:
yield False
__all__ = ["Korean"]