nltk.lm.api.Smoothing

class documentation

class Smoothing: (source)

Known subclasses: nltk.lm.smoothing.KneserNey, nltk.lm.smoothing.WittenBell

Constructor: Smoothing(vocabulary, counter)

Ngram Smoothing Interface

Implements Chen & Goodman 1995's idea that all smoothing algorithms have certain features in common. This should ideally allow smoothing algorithms to work both with Backoff and Interpolation.

Method	`__init__`	No summary
Method	`alpha_gamma`	Undocumented
Method	`unigram_score`	Undocumented
Instance Variable	`counts`	Undocumented
Instance Variable	`vocab`	Undocumented

def __init__(self, vocabulary, counter): (source) ¶

overridden in nltk.lm.smoothing.KneserNey, nltk.lm.smoothing.WittenBell

Parameters
vocabulary:nltk.lm.vocab.Vocabulary	The Ngram vocabulary object.
counter:nltk.lm.counter.NgramCounter	The counts of the vocabulary items.

@abstractmethod
def alpha_gamma(self, word, context): (source) ¶

overridden in nltk.lm.smoothing.KneserNey, nltk.lm.smoothing.WittenBell

Undocumented

@abstractmethod
def unigram_score(self, word): (source) ¶

overridden in nltk.lm.smoothing.KneserNey, nltk.lm.smoothing.WittenBell

Undocumented

counts = (source) ¶

Undocumented

vocab = (source) ¶

Undocumented