218 lines
7.8 KiB
Python
218 lines
7.8 KiB
Python
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from typing import Callable, Dict, List, Optional, Tuple
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from thinc.api import Model
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from ...pipeline import Lemmatizer
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from ...pipeline.lemmatizer import lemmatizer_score
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from ...symbols import POS
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from ...tokens import Token
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from ...vocab import Vocab
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PUNCT_RULES = {"«": '"', "»": '"'}
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class RussianLemmatizer(Lemmatizer):
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def __init__(
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self,
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vocab: Vocab,
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model: Optional[Model],
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name: str = "lemmatizer",
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*,
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mode: str = "pymorphy3",
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overwrite: bool = False,
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scorer: Optional[Callable] = lemmatizer_score,
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) -> None:
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if mode in {"pymorphy2", "pymorphy2_lookup"}:
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try:
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from pymorphy2 import MorphAnalyzer
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except ImportError:
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raise ImportError(
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"The lemmatizer mode 'pymorphy2' requires the "
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"pymorphy2 library and dictionaries. Install them with: "
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"pip install pymorphy2"
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"# for Ukrainian dictionaries:"
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"pip install pymorphy2-dicts-uk"
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) from None
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if getattr(self, "_morph", None) is None:
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self._morph = MorphAnalyzer(lang="ru")
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elif mode in {"pymorphy3", "pymorphy3_lookup"}:
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try:
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from pymorphy3 import MorphAnalyzer
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except ImportError:
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raise ImportError(
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"The lemmatizer mode 'pymorphy3' requires the "
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"pymorphy3 library and dictionaries. Install them with: "
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"pip install pymorphy3"
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"# for Ukrainian dictionaries:"
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"pip install pymorphy3-dicts-uk"
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) from None
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if getattr(self, "_morph", None) is None:
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self._morph = MorphAnalyzer(lang="ru")
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super().__init__(
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vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
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)
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def _pymorphy_lemmatize(self, token: Token) -> List[str]:
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string = token.text
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univ_pos = token.pos_
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morphology = token.morph.to_dict()
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if univ_pos == "PUNCT":
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return [PUNCT_RULES.get(string, string)]
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if univ_pos not in ("ADJ", "DET", "NOUN", "NUM", "PRON", "PROPN", "VERB"):
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return self._pymorphy_lookup_lemmatize(token)
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analyses = self._morph.parse(string)
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filtered_analyses = []
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for analysis in analyses:
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if not analysis.is_known:
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# Skip suggested parse variant for unknown word for pymorphy
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continue
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analysis_pos, _ = oc2ud(str(analysis.tag))
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if (
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analysis_pos == univ_pos
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or (analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN"))
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or ((analysis_pos == "PRON") and (univ_pos == "DET"))
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):
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filtered_analyses.append(analysis)
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if not len(filtered_analyses):
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return [string.lower()]
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if morphology is None or (len(morphology) == 1 and POS in morphology):
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return list(
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dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
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)
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if univ_pos in ("ADJ", "DET", "NOUN", "PROPN"):
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features_to_compare = ["Case", "Number", "Gender"]
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elif univ_pos == "NUM":
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features_to_compare = ["Case", "Gender"]
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elif univ_pos == "PRON":
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features_to_compare = ["Case", "Number", "Gender", "Person"]
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else: # VERB
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features_to_compare = [
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"Aspect",
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"Gender",
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"Mood",
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"Number",
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"Tense",
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"VerbForm",
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"Voice",
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]
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analyses, filtered_analyses = filtered_analyses, []
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for analysis in analyses:
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_, analysis_morph = oc2ud(str(analysis.tag))
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for feature in features_to_compare:
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if (
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feature in morphology
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and feature in analysis_morph
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and morphology[feature].lower() != analysis_morph[feature].lower()
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):
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break
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else:
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filtered_analyses.append(analysis)
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if not len(filtered_analyses):
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return [string.lower()]
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return list(
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dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
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)
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def _pymorphy_lookup_lemmatize(self, token: Token) -> List[str]:
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string = token.text
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analyses = self._morph.parse(string)
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# often multiple forms would derive from the same normal form
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# thus check _unique_ normal forms
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normal_forms = set([an.normal_form for an in analyses])
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if len(normal_forms) == 1:
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return [next(iter(normal_forms))]
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return [string]
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def pymorphy2_lemmatize(self, token: Token) -> List[str]:
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return self._pymorphy_lemmatize(token)
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def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
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return self._pymorphy_lookup_lemmatize(token)
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def pymorphy3_lemmatize(self, token: Token) -> List[str]:
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return self._pymorphy_lemmatize(token)
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def pymorphy3_lookup_lemmatize(self, token: Token) -> List[str]:
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return self._pymorphy_lookup_lemmatize(token)
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def oc2ud(oc_tag: str) -> Tuple[str, Dict[str, str]]:
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gram_map = {
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"_POS": {
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"ADJF": "ADJ",
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"ADJS": "ADJ",
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"ADVB": "ADV",
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"Apro": "DET",
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"COMP": "ADJ", # Can also be an ADV - unchangeable
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"CONJ": "CCONJ", # Can also be a SCONJ - both unchangeable ones
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"GRND": "VERB",
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"INFN": "VERB",
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"INTJ": "INTJ",
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"NOUN": "NOUN",
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"NPRO": "PRON",
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"NUMR": "NUM",
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"NUMB": "NUM",
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"PNCT": "PUNCT",
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"PRCL": "PART",
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"PREP": "ADP",
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"PRTF": "VERB",
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"PRTS": "VERB",
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"VERB": "VERB",
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},
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"Animacy": {"anim": "Anim", "inan": "Inan"},
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"Aspect": {"impf": "Imp", "perf": "Perf"},
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"Case": {
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"ablt": "Ins",
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"accs": "Acc",
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"datv": "Dat",
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"gen1": "Gen",
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"gen2": "Gen",
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"gent": "Gen",
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"loc2": "Loc",
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"loct": "Loc",
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"nomn": "Nom",
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"voct": "Voc",
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},
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"Degree": {"COMP": "Cmp", "Supr": "Sup"},
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"Gender": {"femn": "Fem", "masc": "Masc", "neut": "Neut"},
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"Mood": {"impr": "Imp", "indc": "Ind"},
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"Number": {"plur": "Plur", "sing": "Sing"},
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"NumForm": {"NUMB": "Digit"},
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"Person": {"1per": "1", "2per": "2", "3per": "3", "excl": "2", "incl": "1"},
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"Tense": {"futr": "Fut", "past": "Past", "pres": "Pres"},
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"Variant": {"ADJS": "Brev", "PRTS": "Brev"},
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"VerbForm": {
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"GRND": "Conv",
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"INFN": "Inf",
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"PRTF": "Part",
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"PRTS": "Part",
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"VERB": "Fin",
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},
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"Voice": {"actv": "Act", "pssv": "Pass"},
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"Abbr": {"Abbr": "Yes"},
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}
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pos = "X"
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morphology = dict()
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unmatched = set()
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grams = oc_tag.replace(" ", ",").split(",")
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for gram in grams:
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match = False
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for categ, gmap in sorted(gram_map.items()):
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if gram in gmap:
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match = True
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if categ == "_POS":
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pos = gmap[gram]
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else:
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morphology[categ] = gmap[gram]
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if not match:
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unmatched.add(gram)
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while len(unmatched) > 0:
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gram = unmatched.pop()
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if gram in ("Name", "Patr", "Surn", "Geox", "Orgn"):
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pos = "PROPN"
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elif gram == "Auxt":
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pos = "AUX"
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elif gram == "Pltm":
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morphology["Number"] = "Ptan"
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return pos, morphology
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