Source code for nltk.lm.models

# -*- coding: utf-8 -*-
# Natural Language Toolkit: Language Models
# Copyright (C) 2001-2019 NLTK Project
# Author: Ilia Kurenkov <>
# URL: <>
# For license information, see LICENSE.TXT
"""Language Models"""
from __future__ import division, unicode_literals

from nltk import compat
from nltk.lm.api import LanguageModel, Smoothing
from nltk.lm.smoothing import KneserNey, WittenBell

[docs]@compat.python_2_unicode_compatible class MLE(LanguageModel): """Class for providing MLE ngram model scores. Inherits initialization from BaseNgramModel. """
[docs] def unmasked_score(self, word, context=None): """Returns the MLE score for a word given a context. Args: - word is expcected to be a string - context is expected to be something reasonably convertible to a tuple """ return self.context_counts(context).freq(word)
[docs]@compat.python_2_unicode_compatible class Lidstone(LanguageModel): """Provides Lidstone-smoothed scores. In addition to initialization arguments from BaseNgramModel also requires a number by which to increase the counts, gamma. """ def __init__(self, gamma, *args, **kwargs): super(Lidstone, self).__init__(*args, **kwargs) self.gamma = gamma
[docs] def unmasked_score(self, word, context=None): """Add-one smoothing: Lidstone or Laplace. To see what kind, look at `gamma` attribute on the class. """ counts = self.context_counts(context) word_count = counts[word] norm_count = counts.N() return (word_count + self.gamma) / (norm_count + len(self.vocab) * self.gamma)
[docs]@compat.python_2_unicode_compatible class Laplace(Lidstone): """Implements Laplace (add one) smoothing. Initialization identical to BaseNgramModel because gamma is always 1. """ def __init__(self, *args, **kwargs): super(Laplace, self).__init__(1, *args, **kwargs)
[docs]class InterpolatedLanguageModel(LanguageModel): """Logic common to all interpolated language models. The idea to abstract this comes from Chen & Goodman 1995. """ def __init__(self, smoothing_cls, order, **kwargs): assert issubclass(smoothing_cls, Smoothing) params = kwargs.pop("params", {}) super(InterpolatedLanguageModel, self).__init__(order, **kwargs) self.estimator = smoothing_cls(self.vocab, self.counts, **params)
[docs] def unmasked_score(self, word, context=None): if not context: return self.estimator.unigram_score(word) alpha, gamma = self.estimator.alpha_gamma(word, context) return alpha + gamma * self.unmasked_score(word, context[1:])
[docs]class WittenBellInterpolated(InterpolatedLanguageModel): """Interpolated version of Witten-Bell smoothing.""" def __init__(self, order, **kwargs): super(WittenBellInterpolated, self).__init__(WittenBell, order, **kwargs)
[docs]class KneserNeyInterpolated(InterpolatedLanguageModel): """Interpolated version of Kneser-Ney smoothing.""" def __init__(self, order, discount=0.1, **kwargs): super(KneserNeyInterpolated, self).__init__( KneserNey, order, params={"discount": discount}, **kwargs )