spaCy/spacy/lang/ko/__init__.py
2020-09-30 10:20:14 +02:00

116 lines
3.5 KiB
Python

from typing import Optional, Any, Dict
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
from .lex_attrs import LEX_ATTRS
from ...language import Language
from ...tokens import Doc
from ...compat import copy_reg
from ...scorer import Scorer
from ...symbols import POS
from ...training import validate_examples
from ...util import DummyTokenizer, registry, load_config_from_str
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)
return korean_tokenizer_factory
class KoreanTokenizer(DummyTokenizer):
def __init__(self, nlp: Optional[Language] = None):
self.vocab = nlp.vocab
MeCab = try_mecab_import()
self.mecab_tokenizer = MeCab("-F%f[0],%f[7]")
def __del__(self):
self.mecab_tokenizer.__del__()
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)
token.pos = TAG_MAP[token.tag_][POS]
token.lemma_ = dtoken["lemma"]
doc.user_data["full_tags"] = [dt["tag"] for dt in dtokens]
return doc
def detailed_tokens(self, text: str) -> 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(Language.Defaults):
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}
class Korean(Language):
lang = "ko"
Defaults = KoreanDefaults
def try_mecab_import() -> None:
try:
from natto import MeCab
return MeCab
except ImportError:
raise ImportError(
"Korean support 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
def pickle_korean(instance):
return Korean, tuple()
copy_reg.pickle(Korean, pickle_korean)
__all__ = ["Korean"]