We address the issue of 'topic analysis,' by which is determined a text's topic structure, which indicates what topics are included in a text, and how topics change within the text. We propose a novel approach to this issue, one based on statistical modeling and learning. We use word clusters to represent topics, and employ a finite mixtute model to represent a word distribution within a text. Our experimental results indicate that our method significantly outperforms a method that combines existing techniques.
書誌レコードID
AN10115061
雑誌名
情報処理学会研究報告自然言語処理（NL）
巻
2000
号
86(2000-NL-139)
ページ
61 - 68
発行年
2000-09-21
Notice
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
線形結合モデルを用いたトピック分析
IPSJ-NL00139009.pdf
(853.31KB)
