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100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
# Natural Language Toolkit: Language Models
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#
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# Copyright (C) 2001-2020 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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"""Language Models"""
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from nltk.lm.api import LanguageModel, Smoothing
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from nltk.lm.smoothing import KneserNey, WittenBell
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class MLE(LanguageModel):
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"""Class for providing MLE ngram model scores.
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Inherits initialization from BaseNgramModel.
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"""
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def unmasked_score(self, word, context=None):
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"""Returns the MLE score for a word given a context.
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Args:
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- word is expcected to be a string
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- context is expected to be something reasonably convertible to a tuple
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"""
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return self.context_counts(context).freq(word)
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class Lidstone(LanguageModel):
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"""Provides Lidstone-smoothed scores.
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In addition to initialization arguments from BaseNgramModel also requires
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a number by which to increase the counts, gamma.
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"""
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def __init__(self, gamma, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.gamma = gamma
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def unmasked_score(self, word, context=None):
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"""Add-one smoothing: Lidstone or Laplace.
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To see what kind, look at `gamma` attribute on the class.
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"""
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counts = self.context_counts(context)
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word_count = counts[word]
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norm_count = counts.N()
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return (word_count + self.gamma) / (norm_count + len(self.vocab) * self.gamma)
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class Laplace(Lidstone):
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"""Implements Laplace (add one) smoothing.
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Initialization identical to BaseNgramModel because gamma is always 1.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(1, *args, **kwargs)
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class InterpolatedLanguageModel(LanguageModel):
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"""Logic common to all interpolated language models.
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The idea to abstract this comes from Chen & Goodman 1995.
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Do not instantiate this class directly!
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"""
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def __init__(self, smoothing_cls, order, **kwargs):
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assert issubclass(smoothing_cls, Smoothing)
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params = kwargs.pop("params", {})
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super().__init__(order, **kwargs)
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self.estimator = smoothing_cls(self.vocab, self.counts, **params)
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def unmasked_score(self, word, context=None):
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if not context:
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# The base recursion case: no context, we only have a unigram.
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return self.estimator.unigram_score(word)
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if not self.counts[context]:
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# It can also happen that we have no data for this context.
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# In that case we defer to the lower-order ngram.
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# This is the same as setting alpha to 0 and gamma to 1.
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return self.unmasked_score(word, context[1:])
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alpha, gamma = self.estimator.alpha_gamma(word, context)
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return alpha + gamma * self.unmasked_score(word, context[1:])
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class WittenBellInterpolated(InterpolatedLanguageModel):
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"""Interpolated version of Witten-Bell smoothing."""
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def __init__(self, order, **kwargs):
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super().__init__(WittenBell, order, **kwargs)
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class KneserNeyInterpolated(InterpolatedLanguageModel):
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"""Interpolated version of Kneser-Ney smoothing."""
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def __init__(self, order, discount=0.1, **kwargs):
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super().__init__(KneserNey, order, params={"discount": discount}, **kwargs)
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