Topic Detection and Tracking for News Web Pages

Authors:
Masaki Mori;Takao Miura;Isamu Shioya
Affiliations:
Hosei University, Japan;Hosei University, Japan;Sanno University, Japan
Venue:
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2006

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Abstract

This paper propose a new approach to observe, summarize and track events from a collection of news Web Pages. Given a set of temporal Web pages, we obtain valid timestamp from Web pages and detect events by means of clustering. Then we track events by using KeyGraph based on the clusters and abstract the clusters by using SuffixTree. We examine some experimental results and show the usefulness of our approach.