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abd3f2b65a
Rename Polish lemmatizer method to `pos_lookup` to distinguish it from pure token-based lookup methods.
92 lines
3.6 KiB
Python
92 lines
3.6 KiB
Python
from typing import List, Dict
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from ...pipeline import Lemmatizer
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from ...tokens import Token
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class PolishLemmatizer(Lemmatizer):
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# This lemmatizer implements lookup lemmatization based on the Morfeusz
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# dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS.
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# It utilizes some prefix based improvements for verb and adjectives
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# lemmatization, as well as case-sensitive lemmatization for nouns.
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@classmethod
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def get_lookups_config(cls, mode: str) -> Dict:
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if mode == "pos_lookup":
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return {
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"required_tables": [
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"lemma_lookup_adj",
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"lemma_lookup_adp",
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"lemma_lookup_adv",
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"lemma_lookup_aux",
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"lemma_lookup_noun",
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"lemma_lookup_num",
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"lemma_lookup_part",
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"lemma_lookup_pron",
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"lemma_lookup_verb",
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]
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}
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else:
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return super().get_lookups_config(mode)
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def pos_lookup_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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lookup_pos = univ_pos.lower()
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if univ_pos == "PROPN":
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lookup_pos = "noun"
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lookup_table = self.lookups.get_table("lemma_lookup_" + lookup_pos, {})
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if univ_pos == "NOUN":
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return self.lemmatize_noun(string, morphology, lookup_table)
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if univ_pos != "PROPN":
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string = string.lower()
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if univ_pos == "ADJ":
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return self.lemmatize_adj(string, morphology, lookup_table)
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elif univ_pos == "VERB":
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return self.lemmatize_verb(string, morphology, lookup_table)
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return [lookup_table.get(string, string.lower())]
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def lemmatize_adj(
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self, string: str, morphology: dict, lookup_table: Dict[str, str]
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) -> List[str]:
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# this method utilizes different procedures for adjectives
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# with 'nie' and 'naj' prefixes
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if string[:3] == "nie":
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search_string = string[3:]
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if search_string[:3] == "naj":
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naj_search_string = search_string[3:]
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if naj_search_string in lookup_table:
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return [lookup_table[naj_search_string]]
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if search_string in lookup_table:
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return [lookup_table[search_string]]
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if string[:3] == "naj":
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naj_search_string = string[3:]
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if naj_search_string in lookup_table:
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return [lookup_table[naj_search_string]]
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return [lookup_table.get(string, string)]
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def lemmatize_verb(
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self, string: str, morphology: dict, lookup_table: Dict[str, str]
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) -> List[str]:
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# this method utilizes a different procedure for verbs
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# with 'nie' prefix
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if string[:3] == "nie":
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search_string = string[3:]
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if search_string in lookup_table:
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return [lookup_table[search_string]]
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return [lookup_table.get(string, string)]
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def lemmatize_noun(
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self, string: str, morphology: dict, lookup_table: Dict[str, str]
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) -> List[str]:
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# this method is case-sensitive, in order to work
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# for incorrectly tagged proper names
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if string != string.lower():
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if string.lower() in lookup_table:
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return [lookup_table[string.lower()]]
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elif string in lookup_table:
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return [lookup_table[string]]
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return [string.lower()]
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return [lookup_table.get(string, string)]
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