mirror of https://github.com/explosion/spaCy.git
169 lines
6.2 KiB
Python
169 lines
6.2 KiB
Python
from .symbols import NOUN, VERB, ADJ, PUNCT, PROPN
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from .errors import Errors
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from .lookups import Lookups
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from .parts_of_speech import NAMES as UPOS_NAMES
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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, *args, **kwargs):
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raise NotImplementedError(Errors.E172)
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def __init__(self, lookups, *args, **kwargs):
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"""Initialize a Lemmatizer.
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lookups (Lookups): The lookups object containing the (optional) tables
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"lemma_rules", "lemma_index", "lemma_exc" and "lemma_lookup".
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RETURNS (Lemmatizer): The newly constructed object.
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"""
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if args or kwargs or not isinstance(lookups, Lookups):
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raise ValueError(Errors.E173)
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self.lookups = lookups
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def __call__(self, string, univ_pos, morphology=None):
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"""Lemmatize a string.
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string (str): The string to lemmatize, e.g. the token text.
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univ_pos (str / int): The token's universal part-of-speech tag.
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morphology (dict): The token's morphological features following the
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Universal Dependencies scheme.
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RETURNS (list): The available lemmas for the string.
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"""
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lookup_table = self.lookups.get_table("lemma_lookup", {})
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if "lemma_rules" not in self.lookups:
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return [lookup_table.get(string, string)]
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if isinstance(univ_pos, int):
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univ_pos = UPOS_NAMES.get(univ_pos, "X")
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univ_pos = univ_pos.lower()
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if univ_pos in ("", "eol", "space"):
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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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index_table = self.lookups.get_table("lemma_index", {})
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exc_table = self.lookups.get_table("lemma_exc", {})
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rules_table = self.lookups.get_table("lemma_rules", {})
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if not any((index_table.get(univ_pos), exc_table.get(univ_pos), rules_table.get(univ_pos))):
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if univ_pos == "propn":
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return [string]
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else:
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return [string.lower()]
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lemmas = self.lemmatize(
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string,
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index_table.get(univ_pos, {}),
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exc_table.get(univ_pos, {}),
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rules_table.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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univ_pos (str / int): The token's universal part-of-speech tag.
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morphology (dict): The token's morphological features following the
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Universal Dependencies scheme.
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"""
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if morphology is None:
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morphology = {}
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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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):
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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 morphology.get("VerbForm") == "inf":
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return True
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elif morphology.get("VerbForm") == "none":
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return True
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elif morphology.get("Degree") == "pos":
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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 det(self, string, morphology=None):
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return self(string, "det", morphology)
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def pron(self, string, morphology=None):
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return self(string, "pron", morphology)
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def adp(self, string, morphology=None):
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return self(string, "adp", morphology)
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def num(self, string, morphology=None):
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return self(string, "num", 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, orth=None):
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"""Look up a lemma in the table, if available. If no lemma is found,
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the original string is returned.
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string (str): The original string.
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orth (int): Optional hash of the string to look up. If not set, the
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string will be used and hashed.
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RETURNS (str): The lemma if the string was found, otherwise the
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original string.
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"""
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lookup_table = self.lookups.get_table("lemma_lookup", {})
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key = orth if orth is not None else string
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if key in lookup_table:
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return lookup_table[key]
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return string
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def lemmatize(self, 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(dict.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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