Algorithms for Extracting Topic across Different Types of Documents

Authors:
Shoichi Nakamura;Saori Chiba;Hirokazu Shirai;Hiroaki Kaminaga;Setsuo Yokoyama;Youzou Miyadera
Affiliations:
Department of Computer Science and Mathematics, Fukushima University, Fukushima, Japan 960-1296;Department of Computer Science and Mathematics, Fukushima University, Fukushima, Japan 960-1296;Division of Natural Science, Tokyo Gakugei University, Tokyo, Japan 148-8501;Department of Computer Science and Mathematics, Fukushima University, Fukushima, Japan 960-1296;Division of Natural Science, Tokyo Gakugei University, Tokyo, Japan 148-8501;Division of Natural Science, Tokyo Gakugei University, Tokyo, Japan 148-8501
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Year:
2009

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Abstract

Clever management of the various types of documents used in intelligent activities and their efficient utilization are important. However, most available methods target only a single type of document (e-mails, Web pages, etc.). A more promising approach is topic-centered document management. Algorithms are described for extracting topics across various of types of documents. Moreover, a topic-centered document management system is described that is based on grouping by topics.