Topic-dependent N-gram models based on optimization of context lengths in LDA

Akira Nakamura, Satoru Hayamizu

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper describes a method that improves accuracy of N-gram language models which can be applied to on-line applications. The precision of a long-distance language model including LDA is influenced by a context length, or a length of the history used for prediction. In the proposed method, each of multiple LDA units estimates an optimum context length separately, then those predictions are integrated and N-gram probabilities are calculated. The method directly estimates the optimum context length suitable for prediction. Results show the method improves topic-dependent N-gram probabilities, particularly of a word related to specific topics, yielding higher and more stable performance comparing to an existing method.

本文言語English
ホスト出版物のタイトルProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
出版社International Speech Communication Association
ページ3066-3069
ページ数4
出版ステータスPublished - 2010
外部発表はい

出版物シリーズ

名前Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing

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