mirror of
https://github.com/explosion/spaCy.git
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136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
# coding: utf8
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from __future__ import unicode_literals
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from collections import OrderedDict
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from .symbols import POS, NOUN, VERB, ADJ, PUNCT, PROPN
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from .symbols import VerbForm_inf, VerbForm_none, Number_sing, Degree_pos
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class Lemmatizer(object):
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"""
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The Lemmatizer supports simple part-of-speech-sensitive suffix rules and
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lookup tables.
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DOCS: https://spacy.io/api/lemmatizer
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"""
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@classmethod
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def load(cls, path, index=None, exc=None, rules=None, lookup=None):
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return cls(index, exc, rules, lookup)
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def __init__(self, index=None, exceptions=None, rules=None, lookup=None):
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self.index = index
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self.exc = exceptions
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self.rules = rules
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self.lookup_table = lookup if lookup is not None else {}
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def __call__(self, string, univ_pos, morphology=None):
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if not self.rules:
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return [self.lookup_table.get(string, string)]
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if univ_pos in (NOUN, "NOUN", "noun"):
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univ_pos = "noun"
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elif univ_pos in (VERB, "VERB", "verb"):
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univ_pos = "verb"
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elif univ_pos in (ADJ, "ADJ", "adj"):
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univ_pos = "adj"
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elif univ_pos in (PUNCT, "PUNCT", "punct"):
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univ_pos = "punct"
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elif univ_pos in (PROPN, "PROPN"):
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return [string]
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else:
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return [string.lower()]
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# See Issue #435 for example of where this logic is requied.
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if self.is_base_form(univ_pos, morphology):
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return [string.lower()]
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lemmas = lemmatize(
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string,
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self.index.get(univ_pos, {}),
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self.exc.get(univ_pos, {}),
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self.rules.get(univ_pos, []),
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)
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return lemmas
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def is_base_form(self, univ_pos, morphology=None):
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"""
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Check whether we're dealing with an uninflected paradigm, so we can
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avoid lemmatization entirely.
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"""
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morphology = {} if morphology is None else morphology
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others = [
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key
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for key in morphology
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if key not in (POS, "Number", "POS", "VerbForm", "Tense")
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]
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if univ_pos == "noun" and morphology.get("Number") == "sing":
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return True
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elif univ_pos == "verb" and morphology.get("VerbForm") == "inf":
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return True
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# This maps 'VBP' to base form -- probably just need 'IS_BASE'
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# morphology
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elif univ_pos == "verb" and (
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morphology.get("VerbForm") == "fin"
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and morphology.get("Tense") == "pres"
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and morphology.get("Number") is None
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and not others
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):
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return True
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elif univ_pos == "adj" and morphology.get("Degree") == "pos":
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return True
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elif VerbForm_inf in morphology:
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return True
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elif VerbForm_none in morphology:
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return True
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elif Number_sing in morphology:
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return True
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elif Degree_pos in morphology:
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return True
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else:
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return False
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def noun(self, string, morphology=None):
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return self(string, "noun", morphology)
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def verb(self, string, morphology=None):
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return self(string, "verb", morphology)
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def adj(self, string, morphology=None):
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return self(string, "adj", morphology)
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def punct(self, string, morphology=None):
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return self(string, "punct", morphology)
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def lookup(self, string):
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if string in self.lookup_table:
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return self.lookup_table[string]
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return string
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def lemmatize(string, index, exceptions, rules):
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orig = string
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string = string.lower()
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forms = []
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oov_forms = []
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for old, new in rules:
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if string.endswith(old):
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form = string[: len(string) - len(old)] + new
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if not form:
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pass
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elif form in index or not form.isalpha():
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forms.append(form)
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else:
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oov_forms.append(form)
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# Remove duplicates but preserve the ordering of applied "rules"
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forms = list(OrderedDict.fromkeys(forms))
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# Put exceptions at the front of the list, so they get priority.
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# This is a dodgy heuristic -- but it's the best we can do until we get
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# frequencies on this. We can at least prune out problematic exceptions,
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# if they shadow more frequent analyses.
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for form in exceptions.get(string, []):
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if form not in forms:
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forms.insert(0, form)
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if not forms:
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forms.extend(oov_forms)
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if not forms:
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forms.append(orig)
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return forms
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