mirror of
https://github.com/explosion/spaCy.git
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d17afb4826
* Initial Spanish lemmatizer * Handle merged verb+pron(s) multi-word tokens * Use VERB for AUX rule lookup * Add morph to lemma cache key * Fix aux lookups, minor refactoring * Improve verb+pron handling * Move verb+pron handling into its own method * Check for exceptions (primarily for se) * Collect pronouns in the same (not reversed) order * Only add modified possible lemmas
424 lines
16 KiB
Python
424 lines
16 KiB
Python
from typing import List, Optional, Tuple
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import re
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from ...pipeline import Lemmatizer
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from ...tokens import Token
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class SpanishLemmatizer(Lemmatizer):
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"""
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Spanish rule-based lemmatizer with morph-based rule selection.
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"""
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@classmethod
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def get_lookups_config(cls, mode: str) -> Tuple[List[str], List[str]]:
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if mode == "rule":
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required = ["lemma_rules", "lemma_rules_groups", "lemma_index", "lemma_exc"]
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return (required, [])
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else:
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return super().get_lookups_config(mode)
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def rule_lemmatize(self, token: Token) -> List[str]:
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cache_key = (token.orth, token.pos, str(token.morph))
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if cache_key in self.cache:
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return self.cache[cache_key]
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string = token.text
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pos = token.pos_.lower()
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features = set(token.morph)
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if pos in ("", "eol", "space"):
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return [string.lower()]
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if pos in (
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"adp",
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"cconj",
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"intj",
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"part",
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"propn",
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"punct",
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"sconj",
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"sym",
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"x",
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):
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if token.is_sent_start and pos != "propn":
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return [string.lower()]
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else:
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return [string]
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string = string.lower()
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exc = self.lookups.get_table("lemma_exc").get(pos, {}).get(string)
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if exc is not None:
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lemmas = list(exc)
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else:
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if pos == "aux":
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rule_pos = "verb"
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else:
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rule_pos = pos
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rule = self.select_rule(rule_pos, features)
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index = self.lookups.get_table("lemma_index").get(rule_pos, [])
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lemmas = getattr(self, "lemmatize_" + rule_pos)(
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string, features, rule, index
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)
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# Remove duplicates but preserve the ordering
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lemmas = list(dict.fromkeys(lemmas))
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self.cache[cache_key] = lemmas
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return lemmas
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def select_rule(self, pos: str, features: List[str]) -> Optional[str]:
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groups = self.lookups.get_table("lemma_rules_groups")
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if pos in groups:
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for group in groups[pos]:
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if set(group[1]).issubset(features):
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return group[0]
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return None
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def lemmatize_adj(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize an adjective.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# Initialize empty lists for the generated lemmas
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possible_lemmas = []
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selected_lemmas = []
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# Apply lemmatization rules
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for old, new in self.lookups.get_table("lemma_rules").get(rule, []):
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possible_lemma = re.sub(old + "$", new, word)
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if possible_lemma != word:
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possible_lemmas.append(possible_lemma)
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# Additional rule for plurals that go from esdrújula to grave and end in
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# 'n' or 's', e.g., jóvenes -> joven
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additional_lemmas = []
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if "Number=Plur" in features:
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for possible_lemma in possible_lemmas:
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if possible_lemma.endswith("n") or possible_lemma.endswith("s"):
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for old, new in self.lookups.get_table("lemma_rules").get(
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"accents", []
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):
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additional_lemmas.append(re.sub(old, new, possible_lemma))
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possible_lemmas.extend(additional_lemmas)
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for lemma in possible_lemmas:
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if lemma in index:
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selected_lemmas.append(lemma)
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# If one or more of the created possible lemmas are in the lookup list,
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# return all of them
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if len(selected_lemmas) > 0:
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return selected_lemmas
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elif len(possible_lemmas) > 0:
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return possible_lemmas
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else:
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return [word]
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def lemmatize_adv(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize an adverb.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# Apply lemmatization rules
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for old, new in self.lookups.get_table("lemma_rules").get("adverbs", []):
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if word == old:
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return [new]
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# If none of the rules applies, return the original word
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return [word]
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def lemmatize_det(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize a determiner.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# Initialize empty lists for the generated lemmas
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possible_lemmas = []
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selected_lemmas = []
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# First, search in rules specific to determiners
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for old, new in self.lookups.get_table("lemma_rules").get("det", []):
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if word == old:
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return [new]
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# If none of the specfic rules apply, search in the common rules for
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# determiners and pronouns that follow a unique pattern for
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# lemmatization. If the word is in the list, return the corresponding
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# lemma.
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for old, new in self.lookups.get_table("lemma_rules").get(
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"det_and_pron_fixed", []
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):
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if word == old:
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return [new]
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# If the word is not in the list of unique determiners and pronouns,
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# apply general rules of lemmatization. Include the original word in the # list of possible lemmas.
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for old, new in self.lookups.get_table("lemma_rules").get(
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"det_and_pron_general", []
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):
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possible_lemma = re.sub(old + "$", new, word)
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possible_lemmas.append(possible_lemma)
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possible_lemmas.append(word)
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if len(possible_lemmas) == 1:
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return possible_lemmas
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elif len(possible_lemmas) > 1:
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for lemma in possible_lemmas:
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if lemma in index:
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selected_lemmas.append(lemma)
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if len(selected_lemmas) >= 1:
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return selected_lemmas
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else:
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return possible_lemmas
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def lemmatize_noun(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize a noun.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# Initialize empty lists for the generated lemmas
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possible_lemmas = []
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selected_lemmas = []
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# Apply lemmatization rules
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for old, new in self.lookups.get_table("lemma_rules").get(rule, []):
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possible_lemma = re.sub(old + "$", new, word)
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if possible_lemma != word:
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possible_lemmas.append(possible_lemma)
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# Additional rule for plurals that go from esdrújula to grave and end in
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# 'n' or 's', e.g., órdenes -> orden, exámenes -> examen
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additional_lemmas = []
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if "Number=Plur" in features:
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for possible_lemma in possible_lemmas:
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if possible_lemma.endswith("n") or possible_lemma.endswith("s"):
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for old, new in self.lookups.get_table("lemma_rules").get(
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"accents", []
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):
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additional_lemmas.append(re.sub(old, new, possible_lemma))
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possible_lemmas.extend(additional_lemmas)
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for lemma in possible_lemmas:
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if lemma in index:
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selected_lemmas.append(lemma)
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# If one or more of the created possible lemmas are in the lookup list,
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# return all of them
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if len(selected_lemmas) > 0:
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return selected_lemmas
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elif len(possible_lemmas) > 0:
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return possible_lemmas
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else:
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return [word]
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def lemmatize_num(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize a numeral.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# If the word is in the list of rules for numerals, return the
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# corresponding lemma
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for old, new in self.lookups.get_table("lemma_rules").get("num", []):
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if word == old:
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return [new]
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# Normalize punctuation
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splitted_word = word.split(",")
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if re.search(r"(\.)([0-9]{3})$", splitted_word[0]):
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word = re.sub(r"\.", r"", word)
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word = re.sub(r",", r".", word)
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return [word]
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def lemmatize_pron(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize a pronoun.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# Initialize empty lists for the generated lemmas
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possible_lemmas = []
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selected_lemmas = []
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# First, search in rules specific to pronouns
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for old, new in self.lookups.get_table("lemma_rules").get("pron", []):
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if word == old:
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return [new]
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# If none of the specfic rules apply, search in the common rules for
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# determiners and pronouns that follow a unique pattern for
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# lemmatization. If the word is in the list, return the corresponding
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# lemma.
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for old, new in self.lookups.get_table("lemma_rules").get(
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"det_and_pron_fixed", []
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):
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if word == old:
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return [new]
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# If the word is not in the list of unique determiners and pronouns,
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# apply general rules of lemmatization. Include the original word in the
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# list of possible lemmas.
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for old, new in self.lookups.get_table("lemma_rules").get(
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"det_and_pron_general", []
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):
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possible_lemma = re.sub(old + "$", new, word)
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if possible_lemma != word:
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possible_lemmas.append(possible_lemma)
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possible_lemmas.append(word)
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if len(possible_lemmas) == 1:
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return possible_lemmas
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elif len(possible_lemmas) > 1:
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for lemma in possible_lemmas:
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if lemma in index:
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selected_lemmas.append(lemma)
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if len(selected_lemmas) >= 1:
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return selected_lemmas
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else:
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return possible_lemmas
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def lemmatize_verb(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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"""
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Lemmatize a verb.
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word (str): The word to lemmatize.
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features (List[str]): The morphological features as a list of Feat=Val
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pairs.
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index (List[str]): The POS-specific lookup list.
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RETURNS (List[str]): The list of lemmas.
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"""
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# Exceptions for verb+pronoun(s)
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if "PronType=Prs" in features:
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return self.lemmatize_verb_pron(word, features, rule, index)
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# Initialize empty lists for the generated lemmas
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possible_lemmas = []
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selected_lemmas = []
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# Apply lemmatization rules
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for old, new in self.lookups.get_table("lemma_rules").get(rule, []):
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possible_lemma = re.sub(old + "$", new, word)
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if possible_lemma != word:
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possible_lemmas.append(possible_lemma)
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for lemma in possible_lemmas:
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if lemma in index:
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selected_lemmas.append(lemma)
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if len(selected_lemmas) == 0:
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# If none of the possible lemmas are in the lookup list,
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# apply vocalic alternation rules and search in the lookup list
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# again
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for lemma in possible_lemmas:
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for old, new in self.lookups.get_table("lemma_rules").get(
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"voc_alt_1", []
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):
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if old in lemma:
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for i, char in enumerate(lemma):
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if char == old:
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voc_alt_lemma = lemma[:i] + new + lemma[i + 1 :]
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if voc_alt_lemma in index:
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selected_lemmas.append(voc_alt_lemma)
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for old, new in self.lookups.get_table("lemma_rules").get(
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"voc_alt_2", []
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):
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if old in lemma:
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voc_alt_lemma = lemma.replace(old, new, 1)
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if voc_alt_lemma in index:
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selected_lemmas.append(voc_alt_lemma)
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# Additional rule for verbs that lose the accent mark when lemmatized,
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# e.g., amplían -> ampliar
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additional_lemmas = []
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for possible_lemma in possible_lemmas:
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for old, new in self.lookups.get_table("lemma_rules").get("accents", []):
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additional_lemmas.append(re.sub(old, new, possible_lemma))
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possible_lemmas.extend(additional_lemmas)
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# If one or more of the created possible lemmas are in the lookup list,
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# return all of them
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if len(selected_lemmas) > 0:
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return selected_lemmas
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elif len(possible_lemmas) > 0:
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return possible_lemmas
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else:
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return [word]
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def lemmatize_verb_pron(
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self, word: str, features: List[str], rule: str, index: List[str]
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) -> List[str]:
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# Strip and collect pronouns
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pron_patt = "^(.*?)([mts]e|l[aeo]s?|n?os)$"
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prons = []
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verb = word
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m = re.search(pron_patt, verb)
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while m is not None and len(prons) <= 3:
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verb = re.sub(m.group(2) + "$", "", verb)
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prons = [m.group(2)] + prons
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m = re.search(pron_patt, verb)
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# Strip accents from verb form
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for old, new in self.lookups.get_table("lemma_rules").get("accents", []):
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verb = re.sub(old, new, verb)
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# Lemmatize the verb and pronouns, checking for exceptions
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exc = self.lookups.get_table("lemma_exc").get("verb", {}).get(verb)
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if exc is not None:
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verb_lemma = exc[0]
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else:
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rule = self.select_rule("verb", features)
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verb_lemma = self.lemmatize_verb(
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verb, features - {"PronType=Prs"}, rule, index
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)[0]
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pron_lemmas = []
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for pron in prons:
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exc = self.lookups.get_table("lemma_exc").get("pron", {}).get(pron)
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if exc is not None:
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pron_lemmas.append(exc[0])
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else:
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rule = self.select_rule("pron", features)
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pron_lemmas.append(self.lemmatize_pron(pron, features, rule, index)[0])
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return [verb_lemma + " " + " ".join(pron_lemmas)]
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