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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Kneser–Ney smoothing ： ウィキペディア英語版 Kneser–Ney smoothing Kneser–Ney smoothing is a method primarily used to calculate the probability distribution of ''n''-grams in a document based on their histories.〔('A Bayesian Interpretation of Interpolated Kneser-Ney NUS School of Computing Technical Report TRA2/06' )〕 It is widely considered the most effective method of smoothing due to its use of absolute discounting by subtracting a fixed value from the probability's lower order terms to omit ''n''-grams with lower frequencies. This approach has been considered equally effective for both higher and lower order ''n''-grams. A common example that illustrates the concept behind this method is the frequency of the bigram "San Francisco". If it appears several times in a training corpus, the frequency of the unigram "Francisco" will also be high. Relying on only the unigram frequency to predict the frequencies of ''n''-grams leads to skewed results;〔('Brown University: Introduction to Computational Linguistics ' )〕 however, Kneser–Ney smoothing corrects this by considering the frequency of the unigram in relation to possible words preceding it. ==Method== The equation for bigram probabilities are as follows: $$ p_(w_i|w_) = \frac) - \delta,0)},w')} + p_(w_i) 〔('Kneser Ney Smoothing Explained' )〕 This equation can be extended to n-grams: $$ p_(w_i|w_^) = \frac,w_) - \delta,0)}^)} + \frac^)} (c(w_^))p_(w_i|w_^) 〔('NLP Tutorial: Smoothing' )〕 The probability $p\_(w\_i)$ for a single word is the number of times it appears after any other word divided by the number of words in the corpus, which is represented by the function $c(w\_,w\_)$. The parameter $\backslash delta$ is a constant which denotes the discount value subtracted from the count of each n-gram, usually between 0 and 1. This model uses the concept of absolute-discounting interpolation which incorporates information from higher and lower order language models. The addition of the term for lower order n-grams adds more weight to the overall probability when the count for the higher order n-grams is zero.〔('An empirical study of smoothing techniques for language modeling' )〕 Similarly, the weight of the lower order model decreases when the count of the n-gram is non zero. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Kneser–Ney smoothing」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.