# Natural Language Toolkit: Language Models
#
# Copyright (C) 2001-2023 NLTK Project
# Author: Ilia Kurenkov <ilia.kurenkov@gmail.com>
# Manu Joseph <manujosephv@gmail.com>
# URL: <https://www.nltk.org/>
# For license information, see LICENSE.TXT
"""Language Models"""
from nltk.lm.api import LanguageModel, Smoothing
from nltk.lm.smoothing import AbsoluteDiscounting, KneserNey, WittenBell
[docs]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 expected to be a string
- context is expected to be something reasonably convertible to a tuple
"""
return self.context_counts(context).freq(word)
[docs]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.
"""
[docs] def __init__(self, gamma, *args, **kwargs):
super().__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]class Laplace(Lidstone):
"""Implements Laplace (add one) smoothing.
Initialization identical to BaseNgramModel because gamma is always 1.
"""
[docs] def __init__(self, *args, **kwargs):
super().__init__(1, *args, **kwargs)
[docs]class StupidBackoff(LanguageModel):
"""Provides StupidBackoff scores.
In addition to initialization arguments from BaseNgramModel also requires
a parameter alpha with which we scale the lower order probabilities.
Note that this is not a true probability distribution as scores for ngrams
of the same order do not sum up to unity.
"""
[docs] def __init__(self, alpha=0.4, *args, **kwargs):
super().__init__(*args, **kwargs)
self.alpha = alpha
[docs] def unmasked_score(self, word, context=None):
if not context:
# Base recursion
return self.counts.unigrams.freq(word)
counts = self.context_counts(context)
word_count = counts[word]
norm_count = counts.N()
if word_count > 0:
return word_count / norm_count
else:
return self.alpha * self.unmasked_score(word, context[1:])
[docs]class InterpolatedLanguageModel(LanguageModel):
"""Logic common to all interpolated language models.
The idea to abstract this comes from Chen & Goodman 1995.
Do not instantiate this class directly!
"""
[docs] def __init__(self, smoothing_cls, order, **kwargs):
params = kwargs.pop("params", {})
super().__init__(order, **kwargs)
self.estimator = smoothing_cls(self.vocab, self.counts, **params)
[docs] def unmasked_score(self, word, context=None):
if not context:
# The base recursion case: no context, we only have a unigram.
return self.estimator.unigram_score(word)
if not self.counts[context]:
# It can also happen that we have no data for this context.
# In that case we defer to the lower-order ngram.
# This is the same as setting alpha to 0 and gamma to 1.
alpha, gamma = 0, 1
else:
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."""
[docs] def __init__(self, order, **kwargs):
super().__init__(WittenBell, order, **kwargs)
[docs]class AbsoluteDiscountingInterpolated(InterpolatedLanguageModel):
"""Interpolated version of smoothing with absolute discount."""
[docs] def __init__(self, order, discount=0.75, **kwargs):
super().__init__(
AbsoluteDiscounting, order, params={"discount": discount}, **kwargs
)
[docs]class KneserNeyInterpolated(InterpolatedLanguageModel):
"""Interpolated version of Kneser-Ney smoothing."""
[docs] def __init__(self, order, discount=0.1, **kwargs):
if not (0 <= discount <= 1):
raise ValueError(
"Discount must be between 0 and 1 for probabilities to sum to unity."
)
super().__init__(
KneserNey, order, params={"discount": discount, "order": order}, **kwargs
)