spaCy/spacy/lang/ru/lemmatizer.py

from typing import Callable, Dict, List, Optional, Tuple

from thinc.api import Model

from ...pipeline import Lemmatizer
from ...pipeline.lemmatizer import lemmatizer_score
from ...symbols import POS
from ...tokens import Token
from ...vocab import Vocab

PUNCT_RULES = {"«": '"', "»": '"'}


class RussianLemmatizer(Lemmatizer):
    def __init__(
        self,
        vocab: Vocab,
        model: Optional[Model],
        name: str = "lemmatizer",
        *,
        mode: str = "pymorphy3",
        overwrite: bool = False,
        scorer: Optional[Callable] = lemmatizer_score,
    ) -> None:
        if mode in {"pymorphy2", "pymorphy2_lookup"}:
            try:
                from pymorphy2 import MorphAnalyzer
            except ImportError:
                raise ImportError(
                    "The lemmatizer mode 'pymorphy2' requires the "
                    "pymorphy2 library and dictionaries. Install them with: "
                    "pip install pymorphy2"
                    "# for Ukrainian dictionaries:"
                    "pip install pymorphy2-dicts-uk"
                ) from None
            if getattr(self, "_morph", None) is None:
                self._morph = MorphAnalyzer(lang="ru")
        elif mode in {"pymorphy3", "pymorphy3_lookup"}:
            try:
                from pymorphy3 import MorphAnalyzer
            except ImportError:
                raise ImportError(
                    "The lemmatizer mode 'pymorphy3' requires the "
                    "pymorphy3 library and dictionaries. Install them with: "
                    "pip install pymorphy3"
                    "# for Ukrainian dictionaries:"
                    "pip install pymorphy3-dicts-uk"
                ) from None
            if getattr(self, "_morph", None) is None:
                self._morph = MorphAnalyzer(lang="ru")
        super().__init__(
            vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
        )

    def _pymorphy_lemmatize(self, token: Token) -> List[str]:
        string = token.text
        univ_pos = token.pos_
        morphology = token.morph.to_dict()
        if univ_pos == "PUNCT":
            return [PUNCT_RULES.get(string, string)]
        if univ_pos not in ("ADJ", "DET", "NOUN", "NUM", "PRON", "PROPN", "VERB"):
            return self._pymorphy_lookup_lemmatize(token)
        analyses = self._morph.parse(string)
        filtered_analyses = []
        for analysis in analyses:
            if not analysis.is_known:
                # Skip suggested parse variant for unknown word for pymorphy
                continue
            analysis_pos, _ = oc2ud(str(analysis.tag))
            if (
                analysis_pos == univ_pos
                or (analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN"))
                or ((analysis_pos == "PRON") and (univ_pos == "DET"))
            ):
                filtered_analyses.append(analysis)
        if not len(filtered_analyses):
            return [string.lower()]
        if morphology is None or (len(morphology) == 1 and POS in morphology):
            return list(
                dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
            )
        if univ_pos in ("ADJ", "DET", "NOUN", "PROPN"):
            features_to_compare = ["Case", "Number", "Gender"]
        elif univ_pos == "NUM":
            features_to_compare = ["Case", "Gender"]
        elif univ_pos == "PRON":
            features_to_compare = ["Case", "Number", "Gender", "Person"]
        else:  # VERB
            features_to_compare = [
                "Aspect",
                "Gender",
                "Mood",
                "Number",
                "Tense",
                "VerbForm",
                "Voice",
            ]
        analyses, filtered_analyses = filtered_analyses, []
        for analysis in analyses:
            _, analysis_morph = oc2ud(str(analysis.tag))
            for feature in features_to_compare:
                if (
                    feature in morphology
                    and feature in analysis_morph
                    and morphology[feature].lower() != analysis_morph[feature].lower()
                ):
                    break
            else:
                filtered_analyses.append(analysis)
        if not len(filtered_analyses):
            return [string.lower()]
        return list(
            dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
        )

    def _pymorphy_lookup_lemmatize(self, token: Token) -> List[str]:
        string = token.text
        analyses = self._morph.parse(string)
        # often multiple forms would derive from the same normal form
        # thus check _unique_ normal forms
        normal_forms = set([an.normal_form for an in analyses])
        if len(normal_forms) == 1:
            return [next(iter(normal_forms))]
        return [string]

    def pymorphy2_lemmatize(self, token: Token) -> List[str]:
        return self._pymorphy_lemmatize(token)

    def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
        return self._pymorphy_lookup_lemmatize(token)

    def pymorphy3_lemmatize(self, token: Token) -> List[str]:
        return self._pymorphy_lemmatize(token)

    def pymorphy3_lookup_lemmatize(self, token: Token) -> List[str]:
        return self._pymorphy_lookup_lemmatize(token)


def oc2ud(oc_tag: str) -> Tuple[str, Dict[str, str]]:
    gram_map = {
        "_POS": {
            "ADJF": "ADJ",
            "ADJS": "ADJ",
            "ADVB": "ADV",
            "Apro": "DET",
            "COMP": "ADJ",  # Can also be an ADV - unchangeable
            "CONJ": "CCONJ",  # Can also be a SCONJ - both unchangeable ones
            "GRND": "VERB",
            "INFN": "VERB",
            "INTJ": "INTJ",
            "NOUN": "NOUN",
            "NPRO": "PRON",
            "NUMR": "NUM",
            "NUMB": "NUM",
            "PNCT": "PUNCT",
            "PRCL": "PART",
            "PREP": "ADP",
            "PRTF": "VERB",
            "PRTS": "VERB",
            "VERB": "VERB",
        },
        "Animacy": {"anim": "Anim", "inan": "Inan"},
        "Aspect": {"impf": "Imp", "perf": "Perf"},
        "Case": {
            "ablt": "Ins",
            "accs": "Acc",
            "datv": "Dat",
            "gen1": "Gen",
            "gen2": "Gen",
            "gent": "Gen",
            "loc2": "Loc",
            "loct": "Loc",
            "nomn": "Nom",
            "voct": "Voc",
        },
        "Degree": {"COMP": "Cmp", "Supr": "Sup"},
        "Gender": {"femn": "Fem", "masc": "Masc", "neut": "Neut"},
        "Mood": {"impr": "Imp", "indc": "Ind"},
        "Number": {"plur": "Plur", "sing": "Sing"},
        "NumForm": {"NUMB": "Digit"},
        "Person": {"1per": "1", "2per": "2", "3per": "3", "excl": "2", "incl": "1"},
        "Tense": {"futr": "Fut", "past": "Past", "pres": "Pres"},
        "Variant": {"ADJS": "Brev", "PRTS": "Brev"},
        "VerbForm": {
            "GRND": "Conv",
            "INFN": "Inf",
            "PRTF": "Part",
            "PRTS": "Part",
            "VERB": "Fin",
        },
        "Voice": {"actv": "Act", "pssv": "Pass"},
        "Abbr": {"Abbr": "Yes"},
    }
    pos = "X"
    morphology = dict()
    unmatched = set()
    grams = oc_tag.replace(" ", ",").split(",")
    for gram in grams:
        match = False
        for categ, gmap in sorted(gram_map.items()):
            if gram in gmap:
                match = True
                if categ == "_POS":
                    pos = gmap[gram]
                else:
                    morphology[categ] = gmap[gram]
        if not match:
            unmatched.add(gram)
    while len(unmatched) > 0:
        gram = unmatched.pop()
        if gram in ("Name", "Patr", "Surn", "Geox", "Orgn"):
            pos = "PROPN"
        elif gram == "Auxt":
            pos = "AUX"
        elif gram == "Pltm":
            morphology["Number"] = "Ptan"
    return pos, morphology