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

# 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