57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# Natural Language Toolkit: Language Model Unit Tests
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#
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# Copyright (C) 2001-2019 NLTK Project
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# Author: Ilia Kurenkov <ilia.kurenkov@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""Smoothing algorithms for language modeling.
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According to Chen & Goodman 1995 these should work with both Backoff and
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Interpolation.
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"""
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from nltk.lm.api import Smoothing
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def _count_non_zero_vals(dictionary):
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return sum(1.0 for c in dictionary.values() if c > 0)
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class WittenBell(Smoothing):
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"""Witten-Bell smoothing."""
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def __init__(self, vocabulary, counter, **kwargs):
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super().__init__(vocabulary, counter, **kwargs)
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def alpha_gamma(self, word, context):
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alpha = self.counts[context].freq(word)
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gamma = self._gamma(context)
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return (1.0 - gamma) * alpha, gamma
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def _gamma(self, context):
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n_plus = _count_non_zero_vals(self.counts[context])
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return n_plus / (n_plus + self.counts[len(context) + 1].N())
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def unigram_score(self, word):
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return self.counts.unigrams.freq(word)
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class KneserNey(Smoothing):
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"""Kneser-Ney Smoothing."""
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def __init__(self, vocabulary, counter, discount=0.1, **kwargs):
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super().__init__(vocabulary, counter, **kwargs)
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self.discount = discount
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def unigram_score(self, word):
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return 1.0 / len(self.vocab)
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def alpha_gamma(self, word, context):
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prefix_counts = self.counts[context]
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prefix_total_ngrams = prefix_counts.N()
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alpha = max(prefix_counts[word] - self.discount, 0.0) / prefix_total_ngrams
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gamma = (
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self.discount * _count_non_zero_vals(prefix_counts) / prefix_total_ngrams
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)
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return alpha, gamma
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