mirror of https://github.com/explosion/spaCy.git
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
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# coding: utf-8
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from __future__ import unicode_literals
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from ...lemmatizer import Lemmatizer
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from ...parts_of_speech import NAMES
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from ...errors import Errors
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class PolishLemmatizer(Lemmatizer):
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# This lemmatizer implements lookup lemmatization based on
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# the Morfeusz dictionary (morfeusz.sgjp.pl/en) by Institute of Computer Science PAS
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# It utilizes some prefix based improvements for
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# verb and adjectives lemmatization, as well as case-sensitive
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# lemmatization for nouns
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def __init__(self, lookups, *args, **kwargs):
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# this lemmatizer is lookup based, so it does not require an index, exceptionlist, or rules
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super().__init__(lookups)
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self.lemma_lookups = {}
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for tag in [
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"ADJ",
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"ADP",
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"ADV",
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"AUX",
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"NOUN",
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"NUM",
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"PART",
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"PRON",
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"VERB",
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"X",
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]:
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self.lemma_lookups[tag] = self.lookups.get_table(
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"lemma_lookup_" + tag.lower(), {}
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)
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self.lemma_lookups["DET"] = self.lemma_lookups["X"]
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self.lemma_lookups["PROPN"] = self.lemma_lookups["NOUN"]
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def __call__(self, string, univ_pos, morphology=None):
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if isinstance(univ_pos, int):
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univ_pos = NAMES.get(univ_pos, "X")
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univ_pos = univ_pos.upper()
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if univ_pos == "NOUN":
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return self.lemmatize_noun(string, morphology)
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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)
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elif univ_pos == "VERB":
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return self.lemmatize_verb(string, morphology)
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lemma_dict = self.lemma_lookups.get(univ_pos, {})
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return [lemma_dict.get(string, string.lower())]
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def lemmatize_adj(self, string, morphology):
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# this method utilizes different procedures for adjectives
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# with 'nie' and 'naj' prefixes
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lemma_dict = self.lemma_lookups["ADJ"]
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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 lemma_dict:
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return [lemma_dict[naj_search_string]]
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if search_string in lemma_dict:
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return [lemma_dict[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 lemma_dict:
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return [lemma_dict[naj_search_string]]
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return [lemma_dict.get(string, string)]
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def lemmatize_verb(self, string, morphology):
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# this method utilizes a different procedure for verbs
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# with 'nie' prefix
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lemma_dict = self.lemma_lookups["VERB"]
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if string[:3] == "nie":
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search_string = string[3:]
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if search_string in lemma_dict:
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return [lemma_dict[search_string]]
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return [lemma_dict.get(string, string)]
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def lemmatize_noun(self, string, morphology):
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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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lemma_dict = self.lemma_lookups["NOUN"]
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if string != string.lower():
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if string.lower() in lemma_dict:
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return [lemma_dict[string.lower()]]
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elif string in lemma_dict:
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return [lemma_dict[string]]
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return [string.lower()]
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return [lemma_dict.get(string, string)]
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def lookup(self, string, orth=None):
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return string.lower()
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def lemmatize(self, string, index, exceptions, rules):
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raise NotImplementedError
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