spaCy/spacy/lang/pl/lemmatizer.py

from typing import List, Dict

from ...pipeline import Lemmatizer
from ...tokens import Token


class PolishLemmatizer(Lemmatizer):
    # This lemmatizer implements lookup lemmatization based on the Morfeusz
    # dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS.
    # It utilizes some prefix based improvements for verb and adjectives
    # lemmatization, as well as case-sensitive lemmatization for nouns.

    @classmethod
    def get_lookups_config(cls, mode: str) -> Dict:
        if mode == "pos_lookup":
            return {
                "required_tables": [
                    "lemma_lookup_adj",
                    "lemma_lookup_adp",
                    "lemma_lookup_adv",
                    "lemma_lookup_aux",
                    "lemma_lookup_noun",
                    "lemma_lookup_num",
                    "lemma_lookup_part",
                    "lemma_lookup_pron",
                    "lemma_lookup_verb",
                ]
            }
        else:
            return super().get_lookups_config(mode)

    def pos_lookup_lemmatize(self, token: Token) -> List[str]:
        string = token.text
        univ_pos = token.pos_
        morphology = token.morph.to_dict()
        lookup_pos = univ_pos.lower()
        if univ_pos == "PROPN":
            lookup_pos = "noun"
        lookup_table = self.lookups.get_table("lemma_lookup_" + lookup_pos, {})
        if univ_pos == "NOUN":
            return self.lemmatize_noun(string, morphology, lookup_table)
        if univ_pos != "PROPN":
            string = string.lower()
        if univ_pos == "ADJ":
            return self.lemmatize_adj(string, morphology, lookup_table)
        elif univ_pos == "VERB":
            return self.lemmatize_verb(string, morphology, lookup_table)
        return [lookup_table.get(string, string.lower())]

    def lemmatize_adj(
        self, string: str, morphology: dict, lookup_table: Dict[str, str]
    ) -> List[str]:
        # this method utilizes different procedures for adjectives
        # with 'nie' and 'naj' prefixes
        if string[:3] == "nie":
            search_string = string[3:]
            if search_string[:3] == "naj":
                naj_search_string = search_string[3:]
                if naj_search_string in lookup_table:
                    return [lookup_table[naj_search_string]]
            if search_string in lookup_table:
                return [lookup_table[search_string]]
        if string[:3] == "naj":
            naj_search_string = string[3:]
            if naj_search_string in lookup_table:
                return [lookup_table[naj_search_string]]
        return [lookup_table.get(string, string)]

    def lemmatize_verb(
        self, string: str, morphology: dict, lookup_table: Dict[str, str]
    ) -> List[str]:
        # this method utilizes a different procedure for verbs
        # with 'nie' prefix
        if string[:3] == "nie":
            search_string = string[3:]
            if search_string in lookup_table:
                return [lookup_table[search_string]]
        return [lookup_table.get(string, string)]

    def lemmatize_noun(
        self, string: str, morphology: dict, lookup_table: Dict[str, str]
    ) -> List[str]:
        # this method is case-sensitive, in order to work
        # for incorrectly tagged proper names
        if string != string.lower():
            if string.lower() in lookup_table:
                return [lookup_table[string.lower()]]
            elif string in lookup_table:
                return [lookup_table[string]]
            return [string.lower()]
        return [lookup_table.get(string, string)]