spaCy/spacy/lang/ja/__init__.py
2018-12-18 15:02:11 +01:00

179 lines
5.3 KiB
Python

# encoding: utf8
from __future__ import unicode_literals, print_function
from ...language import Language
from ...attrs import LANG
from ...tokens import Doc, Token
from ...tokenizer import Tokenizer
from ... import util
from .tag_map import TAG_MAP
import re
from collections import namedtuple
ShortUnitWord = namedtuple("ShortUnitWord", ["surface", "lemma", "pos"])
def try_mecab_import():
"""Mecab is required for Japanese support, so check for it.
It it's not available blow up and explain how to fix it."""
try:
import MeCab
# XXX Is this the right place for this?
Token.set_extension("mecab_tag", default=None)
return MeCab
except ImportError:
raise ImportError(
"Japanese support requires MeCab: "
"https://github.com/SamuraiT/mecab-python3"
)
def resolve_pos(token):
"""If necessary, add a field to the POS tag for UD mapping.
Under Universal Dependencies, sometimes the same Unidic POS tag can
be mapped differently depending on the literal token or its context
in the sentence. This function adds information to the POS tag to
resolve ambiguous mappings.
"""
# NOTE: This is a first take. The rules here are crude approximations.
# For many of these, full dependencies are needed to properly resolve
# PoS mappings.
if token.pos == "連体詞,*,*,*":
if re.match("^[こそあど此其彼]の", token.surface):
return token.pos + ",DET"
if re.match("^[こそあど此其彼]", token.surface):
return token.pos + ",PRON"
else:
return token.pos + ",ADJ"
return token.pos
def detailed_tokens(tokenizer, text):
"""Format Mecab output into a nice data structure, based on Janome."""
tokenizer.parse(text)
node = tokenizer.parseToNode(text)
node = node.next # first node is beginning of sentence and empty, skip it
words = []
while node.posid != 0:
surface = node.surface
base = surface # a default value. Updated if available later.
parts = node.feature.split(",")
pos = ",".join(parts[0:4])
if len(parts) > 7:
# this information is only available for words in the tokenizer dictionary
base = parts[7]
words.append(ShortUnitWord(surface, base, pos))
node = node.next
return words
class JapaneseTokenizer(object):
def __init__(self, cls, nlp=None):
self.vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
MeCab = try_mecab_import()
self.tokenizer = MeCab.Tagger()
self.tokenizer.parseToNode("") # see #2901
def __call__(self, text):
dtokens = detailed_tokens(self.tokenizer, text)
words = [x.surface for x in dtokens]
doc = Doc(self.vocab, words=words, spaces=[False] * len(words))
for token, dtoken in zip(doc, dtokens):
token._.mecab_tag = dtoken.pos
token.tag_ = resolve_pos(dtoken)
token.lemma_ = dtoken.lemma
return doc
# add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
# allow serialization (see #1557)
def to_bytes(self, **exclude):
return b""
def from_bytes(self, bytes_data, **exclude):
return self
def to_disk(self, path, **exclude):
return None
def from_disk(self, path, **exclude):
return self
class JapaneseCharacterSegmenter(object):
def __init__(self, vocab):
self.vocab = vocab
self._presegmenter = self._make_presegmenter(self.vocab)
def _make_presegmenter(self, vocab):
rules = Japanese.Defaults.tokenizer_exceptions
token_match = Japanese.Defaults.token_match
prefix_search = (
util.compile_prefix_regex(Japanese.Defaults.prefixes).search
if Japanese.Defaults.prefixes
else None
)
suffix_search = (
util.compile_suffix_regex(Japanese.Defaults.suffixes).search
if Japanese.Defaults.suffixes
else None
)
infix_finditer = (
util.compile_infix_regex(Japanese.Defaults.infixes).finditer
if Japanese.Defaults.infixes
else None
)
return Tokenizer(
vocab,
rules=rules,
prefix_search=prefix_search,
suffix_search=suffix_search,
infix_finditer=infix_finditer,
token_match=token_match,
)
def __call__(self, text):
words = []
spaces = []
doc = self._presegmenter(text)
for token in doc:
words.extend(list(token.text))
spaces.extend([False] * len(token.text))
spaces[-1] = bool(token.whitespace_)
return Doc(self.vocab, words=words, spaces=spaces)
class JapaneseDefaults(Language.Defaults):
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: "ja"
tag_map = TAG_MAP
use_janome = True
@classmethod
def create_tokenizer(cls, nlp=None):
if cls.use_janome:
return JapaneseTokenizer(cls, nlp)
else:
return JapaneseCharacterSegmenter(nlp.vocab)
class Japanese(Language):
lang = "ja"
Defaults = JapaneseDefaults
Tokenizer = JapaneseTokenizer
def make_doc(self, text):
return self.tokenizer(text)
__all__ = ["Japanese"]