spaCy/spacy/lemmatizer.py

140 lines
4.7 KiB
Python

# coding: utf8
from __future__ import unicode_literals
from collections import OrderedDict
from .symbols import NOUN, VERB, ADJ, PUNCT, PROPN
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, path, index=None, exc=None, rules=None, lookup=None):
return cls(index, exc, rules, lookup)
def __init__(self, index=None, exceptions=None, rules=None, lookup=None):
self.index = index
self.exc = exceptions
self.rules = rules
self.lookup_table = lookup if lookup is not None else {}
def __call__(self, string, univ_pos, morphology=None):
if not self.rules:
return [self.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()]
lemmas = lemmatize(
string,
self.index.get(univ_pos, {}),
self.exc.get(univ_pos, {}),
self.rules.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.
"""
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 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.
"""
key = orth if orth is not None else string
if key in self.lookup_table:
return self.lookup_table[key]
return string
def lemmatize(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(OrderedDict.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