Information visualization using 3D interactive animation
Communications of the ACM - Special issue on graphical user interfaces
Enhanced dynamic queries via movable filters
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic text decomposition using text segments and text themes
Proceedings of the the seventh ACM conference on Hypertext
Scatter/gather browsing communicates the topic structure of a very large text collection
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Experiences with selecting search engines using metasearch
ACM Transactions on Information Systems (TOIS)
Queries? Links? Is there a difference?
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
The paraphrase search assistant: terminological feedback for iterative information seeking
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval: Algorithms and Heuristics
Information Retrieval: Algorithms and Heuristics
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A method for supporting WWW retrieval by constructing a flexible category structure adaptable to the user's search intention is proposed. The method uses categorization viewpoints as a priori knowledge, where a categorization viewpoint is a finite set of consistent category names. A set of documents retrieved by initial keywords is decomposed by categorization viewpoints and each decomposition is scored by clearness or entropy. The user selects an appropriate decomposition by considering the score. The decomposition is recursively performed until a category structure of reasonable size is obtained. Experimental results show that the sets of documents decomposed by the proposed method have higher precision than those decomposed by clustering (K-means). It is also shown that both the scores based on clearness and entropy of the decomposition have relatively high correlation with the precision.