Paper: Topic Analysis Using A Finite Mixture Model

ACL ID W00-1305
Title Topic Analysis Using A Finite Mixture Model
Venue 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora
Session Main Conference
Year 2000

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 represent topics by means of word clusters, and employ a finite mixture model to repre- sent a word distribution within a text. Our experimental results indicate that our method significantly outperforms a method that com- bines existing techniques.