You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# Natural Language Toolkit: Language Model Unit Tests
|
|
#
|
|
# Copyright (C) 2001-2020 NLTK Project
|
|
# Author: Ilia Kurenkov <ilia.kurenkov@gmail.com>
|
|
# URL: <http://nltk.org/>
|
|
# For license information, see LICENSE.TXT
|
|
"""Smoothing algorithms for language modeling.
|
|
|
|
According to Chen & Goodman 1995 these should work with both Backoff and
|
|
Interpolation.
|
|
"""
|
|
|
|
from nltk.lm.api import Smoothing
|
|
|
|
|
|
def _count_non_zero_vals(dictionary):
|
|
return sum(1.0 for c in dictionary.values() if c > 0)
|
|
|
|
|
|
class WittenBell(Smoothing):
|
|
"""Witten-Bell smoothing."""
|
|
|
|
def __init__(self, vocabulary, counter, **kwargs):
|
|
super().__init__(vocabulary, counter, **kwargs)
|
|
|
|
def alpha_gamma(self, word, context):
|
|
alpha = self.counts[context].freq(word)
|
|
gamma = self._gamma(context)
|
|
return (1.0 - gamma) * alpha, gamma
|
|
|
|
def _gamma(self, context):
|
|
n_plus = _count_non_zero_vals(self.counts[context])
|
|
return n_plus / (n_plus + self.counts[len(context) + 1].N())
|
|
|
|
def unigram_score(self, word):
|
|
return self.counts.unigrams.freq(word)
|
|
|
|
|
|
class KneserNey(Smoothing):
|
|
"""Kneser-Ney Smoothing."""
|
|
|
|
def __init__(self, vocabulary, counter, discount=0.1, **kwargs):
|
|
super().__init__(vocabulary, counter, **kwargs)
|
|
self.discount = discount
|
|
|
|
def unigram_score(self, word):
|
|
return 1.0 / len(self.vocab)
|
|
|
|
def alpha_gamma(self, word, context):
|
|
prefix_counts = self.counts[context]
|
|
prefix_total_ngrams = prefix_counts.N()
|
|
alpha = max(prefix_counts[word] - self.discount, 0.0) / prefix_total_ngrams
|
|
gamma = (
|
|
self.discount * _count_non_zero_vals(prefix_counts) / prefix_total_ngrams
|
|
)
|
|
return alpha, gamma
|