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Fix Polish lemmatizer for deserialized models

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Restructure Polish lemmatizer not to depend on lookups data in
`__init__` since the lemmatizer is initialized before the lookups data
is loaded from a saved model. The lookups tables are accessed first in
`__call__` instead once the data is available.
This commit is contained in:
Adriane Boyd 2020-05-26 09:56:12 +02:00
parent 69897b45d8
commit 1eed101be9
1 changed files with 31 additions and 56 deletions
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87
spacy/lang/pl/lemmatizer.py
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@ -6,98 +6,73 @@ from ...parts_of_speech import NAMES
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
def __init__(self, lookups, *args, **kwargs):
# this lemmatizer is lookup based, so it does not require an index, exceptionlist, or rules
super(PolishLemmatizer, self).__init__(lookups)
self.lemma_lookups = {}
for tag in [
"ADJ",
"ADP",
"ADV",
"AUX",
"NOUN",
"NUM",
"PART",
"PRON",
"VERB",
"X",
]:
self.lemma_lookups[tag] = self.lookups.get_table(
"lemma_lookup_" + tag.lower(), {}
)
self.lemma_lookups["DET"] = self.lemma_lookups["X"]
self.lemma_lookups["PROPN"] = self.lemma_lookups["NOUN"]
# 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.
def __call__(self, string, univ_pos, morphology=None):
if isinstance(univ_pos, int):
univ_pos = NAMES.get(univ_pos, "X")
univ_pos = univ_pos.upper()
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)
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)
return self.lemmatize_adj(string, morphology, lookup_table)
elif univ_pos == "VERB":
return self.lemmatize_verb(string, morphology)
return self.lemmatize_verb(string, morphology, lookup_table)
lemma_dict = self.lemma_lookups.get(univ_pos, {})
return [lemma_dict.get(string, string.lower())]
return [lookup_table.get(string, string.lower())]
def lemmatize_adj(self, string, morphology):
def lemmatize_adj(self, string, morphology, lookup_table):
# this method utilizes different procedures for adjectives
# with 'nie' and 'naj' prefixes
lemma_dict = self.lemma_lookups["ADJ"]
if string[:3] == "nie":
search_string = string[3:]
if search_string[:3] == "naj":
naj_search_string = search_string[3:]
if naj_search_string in lemma_dict:
return [lemma_dict[naj_search_string]]
if search_string in lemma_dict:
return [lemma_dict[search_string]]
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 lemma_dict:
return [lemma_dict[naj_search_string]]
if naj_search_string in lookup_table:
return [lookup_table[naj_search_string]]
return [lemma_dict.get(string, string)]
return [lookup_table.get(string, string)]
def lemmatize_verb(self, string, morphology):
def lemmatize_verb(self, string, morphology, lookup_table):
# this method utilizes a different procedure for verbs
# with 'nie' prefix
lemma_dict = self.lemma_lookups["VERB"]
if string[:3] == "nie":
search_string = string[3:]
if search_string in lemma_dict:
return [lemma_dict[search_string]]
if search_string in lookup_table:
return [lookup_table[search_string]]
return [lemma_dict.get(string, string)]
return [lookup_table.get(string, string)]
def lemmatize_noun(self, string, morphology):
def lemmatize_noun(self, string, morphology, lookup_table):
# this method is case-sensitive, in order to work
# for incorrectly tagged proper names
lemma_dict = self.lemma_lookups["NOUN"]
if string != string.lower():
if string.lower() in lemma_dict:
return [lemma_dict[string.lower()]]
elif string in lemma_dict:
return [lemma_dict[string]]
if string.lower() in lookup_table:
return [lookup_table[string.lower()]]
elif string in lookup_table:
return [lookup_table[string]]
return [string.lower()]
return [lemma_dict.get(string, string)]
return [lookup_table.get(string, string)]
def lookup(self, string, orth=None):
return string.lower()