mirror of https://github.com/explosion/spaCy.git
171 lines
6.2 KiB
Python
171 lines
6.2 KiB
Python
from .symbols import NOUN, VERB, ADJ, PUNCT, PROPN
|
|
from .errors import Errors
|
|
from .lookups import Lookups
|
|
|
|
|
|
class Lemmatizer(object):
|
|
"""
|
|
The Lemmatizer supports simple part-of-speech-sensitive suffix rules and
|
|
lookup tables.
|
|
|
|
DOCS: https://spacy.io/api/lemmatizer
|
|
"""
|
|
|
|
@classmethod
|
|
def load(cls, *args, **kwargs):
|
|
raise NotImplementedError(Errors.E172)
|
|
|
|
def __init__(self, lookups, *args, **kwargs):
|
|
"""Initialize a Lemmatizer.
|
|
|
|
lookups (Lookups): The lookups object containing the (optional) tables
|
|
"lemma_rules", "lemma_index", "lemma_exc" and "lemma_lookup".
|
|
RETURNS (Lemmatizer): The newly constructed object.
|
|
"""
|
|
if args or kwargs or not isinstance(lookups, Lookups):
|
|
raise ValueError(Errors.E173)
|
|
self.lookups = lookups
|
|
|
|
def __call__(self, string, univ_pos, morphology=None):
|
|
"""Lemmatize a string.
|
|
|
|
string (unicode): The string to lemmatize, e.g. the token text.
|
|
univ_pos (unicode / int): The token's universal part-of-speech tag.
|
|
morphology (dict): The token's morphological features following the
|
|
Universal Dependencies scheme.
|
|
RETURNS (list): The available lemmas for the string.
|
|
"""
|
|
lookup_table = self.lookups.get_table("lemma_lookup", {})
|
|
if "lemma_rules" not in self.lookups:
|
|
return [lookup_table.get(string, string)]
|
|
if univ_pos in (NOUN, "NOUN", "noun"):
|
|
univ_pos = "noun"
|
|
elif univ_pos in (VERB, "VERB", "verb"):
|
|
univ_pos = "verb"
|
|
elif univ_pos in (ADJ, "ADJ", "adj"):
|
|
univ_pos = "adj"
|
|
elif univ_pos in (PUNCT, "PUNCT", "punct"):
|
|
univ_pos = "punct"
|
|
elif univ_pos in (PROPN, "PROPN"):
|
|
return [string]
|
|
else:
|
|
return [string.lower()]
|
|
# See Issue #435 for example of where this logic is requied.
|
|
if self.is_base_form(univ_pos, morphology):
|
|
return [string.lower()]
|
|
index_table = self.lookups.get_table("lemma_index", {})
|
|
exc_table = self.lookups.get_table("lemma_exc", {})
|
|
rules_table = self.lookups.get_table("lemma_rules", {})
|
|
lemmas = self.lemmatize(
|
|
string,
|
|
index_table.get(univ_pos, {}),
|
|
exc_table.get(univ_pos, {}),
|
|
rules_table.get(univ_pos, []),
|
|
)
|
|
return lemmas
|
|
|
|
def is_base_form(self, univ_pos, morphology=None):
|
|
"""
|
|
Check whether we're dealing with an uninflected paradigm, so we can
|
|
avoid lemmatization entirely.
|
|
|
|
univ_pos (unicode / int): The token's universal part-of-speech tag.
|
|
morphology (dict): The token's morphological features following the
|
|
Universal Dependencies scheme.
|
|
"""
|
|
if morphology is None:
|
|
morphology = {}
|
|
if univ_pos == "noun" and morphology.get("Number") == "sing":
|
|
return True
|
|
elif univ_pos == "verb" and morphology.get("VerbForm") == "inf":
|
|
return True
|
|
# This maps 'VBP' to base form -- probably just need 'IS_BASE'
|
|
# morphology
|
|
elif univ_pos == "verb" and (
|
|
morphology.get("VerbForm") == "fin"
|
|
and morphology.get("Tense") == "pres"
|
|
and morphology.get("Number") is None
|
|
):
|
|
return True
|
|
elif univ_pos == "adj" and morphology.get("Degree") == "pos":
|
|
return True
|
|
elif morphology.get("VerbForm") == "inf":
|
|
return True
|
|
elif morphology.get("VerbForm") == "none":
|
|
return True
|
|
elif morphology.get("VerbForm") == "inf":
|
|
return True
|
|
elif morphology.get("Degree") == "pos":
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def noun(self, string, morphology=None):
|
|
return self(string, "noun", morphology)
|
|
|
|
def verb(self, string, morphology=None):
|
|
return self(string, "verb", morphology)
|
|
|
|
def adj(self, string, morphology=None):
|
|
return self(string, "adj", morphology)
|
|
|
|
def det(self, string, morphology=None):
|
|
return self(string, "det", morphology)
|
|
|
|
def pron(self, string, morphology=None):
|
|
return self(string, "pron", morphology)
|
|
|
|
def adp(self, string, morphology=None):
|
|
return self(string, "adp", morphology)
|
|
|
|
def num(self, string, morphology=None):
|
|
return self(string, "num", morphology)
|
|
|
|
def punct(self, string, morphology=None):
|
|
return self(string, "punct", morphology)
|
|
|
|
def lookup(self, string, orth=None):
|
|
"""Look up a lemma in the table, if available. If no lemma is found,
|
|
the original string is returned.
|
|
|
|
string (unicode): The original string.
|
|
orth (int): Optional hash of the string to look up. If not set, the
|
|
string will be used and hashed.
|
|
RETURNS (unicode): The lemma if the string was found, otherwise the
|
|
original string.
|
|
"""
|
|
lookup_table = self.lookups.get_table("lemma_lookup", {})
|
|
key = orth if orth is not None else string
|
|
if key in lookup_table:
|
|
return lookup_table[key]
|
|
return string
|
|
|
|
def lemmatize(self, string, index, exceptions, rules):
|
|
orig = string
|
|
string = string.lower()
|
|
forms = []
|
|
oov_forms = []
|
|
for old, new in rules:
|
|
if string.endswith(old):
|
|
form = string[: len(string) - len(old)] + new
|
|
if not form:
|
|
pass
|
|
elif form in index or not form.isalpha():
|
|
forms.append(form)
|
|
else:
|
|
oov_forms.append(form)
|
|
# Remove duplicates but preserve the ordering of applied "rules"
|
|
forms = list(dict.fromkeys(forms))
|
|
# Put exceptions at the front of the list, so they get priority.
|
|
# This is a dodgy heuristic -- but it's the best we can do until we get
|
|
# frequencies on this. We can at least prune out problematic exceptions,
|
|
# if they shadow more frequent analyses.
|
|
for form in exceptions.get(string, []):
|
|
if form not in forms:
|
|
forms.insert(0, form)
|
|
if not forms:
|
|
forms.extend(oov_forms)
|
|
if not forms:
|
|
forms.append(orig)
|
|
return forms
|