spaCy/spacy/lang/uk/lemmatizer.py

# coding: utf8
from ...symbols import (
    ADJ, DET, NOUN, NUM, PRON, PROPN, PUNCT, VERB, POS
)
from ...lemmatizer import Lemmatizer


class UkrainianLemmatizer(Lemmatizer):
    _morph = None

    def __init__(self):
        super(UkrainianLemmatizer, self).__init__()
        try:
            from pymorphy2 import MorphAnalyzer
            if UkrainianLemmatizer._morph is None:
                UkrainianLemmatizer._morph = MorphAnalyzer(lang='uk')
        except (ImportError, TypeError):
            raise ImportError(
                'The Ukrainian lemmatizer requires the pymorphy2 library and dictionaries: '
                'try to fix it with'
                '"pip uninstall pymorphy2"'
                '"pip install git+https://github.com/kmike/pymorphy2.git pymorphy2-dicts-uk"')


    def __call__(self, string, univ_pos, morphology=None):
        univ_pos = self.normalize_univ_pos(univ_pos)
        if univ_pos == 'PUNCT':
            return [PUNCT_RULES.get(string, string)]

        if univ_pos not in ('ADJ', 'DET', 'NOUN', 'NUM', 'PRON', 'PROPN', 'VERB'):
            # Skip unchangeable pos
            return [string.lower()]

        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')):
                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(set([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] != analysis_morph[feature]):
                    break
            else:
                filtered_analyses.append(analysis)

        if not len(filtered_analyses):
            return [string.lower()]
        return list(set([analysis.normal_form for analysis in filtered_analyses]))

    @staticmethod
    def normalize_univ_pos(univ_pos):
        if isinstance(univ_pos, str):
            return univ_pos.upper()

        symbols_to_str = {
            ADJ: 'ADJ',
            DET: 'DET',
            NOUN: 'NOUN',
            NUM: 'NUM',
            PRON: 'PRON',
            PROPN: 'PROPN',
            PUNCT: 'PUNCT',
            VERB: 'VERB'
        }
        if univ_pos in symbols_to_str:
            return symbols_to_str[univ_pos]
        return None

    def is_base_form(self, univ_pos, morphology=None):
        # TODO
        raise NotImplementedError

    def det(self, string, morphology=None):
        return self(string, 'det', morphology)

    def num(self, string, morphology=None):
        return self(string, 'num', morphology)

    def pron(self, string, morphology=None):
        return self(string, 'pron', morphology)

    def lookup(self, string):
        analyses = self._morph.parse(string)
        if len(analyses) == 1:
            return analyses[0].normal_form
        return string


def oc2ud(oc_tag):
    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


PUNCT_RULES = {
    "«": "\"",
    "»": "\""
}