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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 Brown clustering ： ウィキペディア英語版 Brown clustering Brown clustering is a hard hierarchical agglomerative clustering problem based on distributional information. It is typically applied to text, grouping words into clusters that are assumed to be semantically related by virtue of their having been embedded in similar contexts. == Introduction == In natural language processing, Brown clustering〔〔 or IBM clustering〔 is a form of hierarchical clustering of words based on the contexts in which they occur, proposed by Peter Brown, Vincent Della Pietra, Peter deSouza, Jennifer Lai, and Robert Mercer of IBM in the context of language modeling. The intuition behind the method is that a class-based language model (also called cluster -gram model〔), i.e. one where probabilities of words are based on the classes (clusters) of previous words, is used to address the data sparsity problem inherent in language modeling. Jurafsky and Martin give the example of a flight reservation system that needs to estimate the likelihood of the bigram "to Shanghai", without having seen this in a training set. The system can obtain a good estimate if it can cluster "Shanghai" with other city names, then make its estimate based on the likelihood of phrases such as "to London", "to Beijing" and "to Denver". 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「Brown clustering」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.