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Python

# -*- coding: utf-8 -*-
# Natural Language Toolkit: Language Model Unit Tests
#
# Copyright (C) 2001-2019 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, discount=0.1, **kwargs):
super(WittenBell, self).__init__(vocabulary, counter, *kwargs)
def alpha_gamma(self, word, context):
gamma = self.gamma(context)
return (1.0 - gamma) * self.alpha(word, context), gamma
def unigram_score(self, word):
return self.counts.unigrams.freq(word)
def alpha(self, word, context):
return self.counts[context].freq(word)
def gamma(self, context):
n_plus = _count_non_zero_vals(self.counts[context])
return n_plus / (n_plus + self.counts[len(context) + 1].N())
class KneserNey(Smoothing):
"""Kneser-Ney Smoothing."""
def __init__(self, vocabulary, counter, discount=0.1, **kwargs):
super(KneserNey, self).__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]
return self.alpha(word, prefix_counts), self.gamma(prefix_counts)
def alpha(self, word, prefix_counts):
return max(prefix_counts[word] - self.discount, 0.0) / prefix_counts.N()
def gamma(self, prefix_counts):
return self.discount * _count_non_zero_vals(prefix_counts) / prefix_counts.N()