An NLP & IR approach to topic detection

Authors:
Hsin-Hsi Chen;Lun-Wei Ku
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
Venue:
Topic detection and tracking
Year:
2002

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Abstract

This paper presents algorithms for Chinese and English-Chinese topic detection. Named entities, other nouns and verbs are cue patterns to relate news stories describing the same event. Lexical translation and name transliteration resolve lexical differences between English and Chinese. A two-threshold scheme determines relevance (irrelevance) between a news story and a topic cluster. Lookahead information deals with ambiguous cases in clustering. The least-recently-used removal strategy models the time factor in such a way that older and unimportant terms will have no effect on clustering. Experimental results show that nouns and verbs as well as the least-recently-used removal strategy outperform other models. The performance of the named-entity-only approach decreases slightly, but it has no overhead of nouns-and-verbs approach with the least-recently-used removal strategy.