Abstract
Dynamically generated topic hierarchies are a popular method of summarizing the results obtained in response to a query in various search applications. However, topic hierarchies generated by statistical techniques tend to be somewhat unintuitive, rigid and inefficient for browsing and disambiguation. In this talk, we propose an alternative approach to query disambiguation and result summarization. The approach uses a fixed set of orthogonal contextual dimensions to summarize and disambiguate search results. A contextual dimension defines a specific type to a context which makes it incomparable to contexts of other types. For the generic search scenario, we propose to use three types of contextual dimensions, namely, concepts, features, and specializations. We use NLP techniques to extract the three types of contexts, and a data mining algorithm to select a subset of contexts that are as distinct i.e., mutually exclusive as possible. Our experimental results show that we can achieve a considerable reduction in the effort required for retrieving relevant documents via the proposed interface.

Biography
Raghu Krishnapuram received his Ph.D. degree in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, in 1987. From 1987 to 1997, he was on the faculty of the Department of Computer Engineering and Computer Science at the University of Missouri, Columbia. From 1997 to 2000, Dr. Krishnapuram was a Full Professor at the Department of Mathematical and Computer Sciences, Colorado School of Mines, Golden, Colorado. Since then, he has been at at IBM India Research Lab, New Delhi. Dr. Krishnapurams research encompasses many aspects of Web mining, information retrieval, e-commerce, fuzzy set theory, neural networks, pattern recognition, computer vision, and image processing. He has published over 160 papers in journals and conferences in these areas. Dr. Krishnapuram is an IEEE Fellow, and a co-author with J. Bezdek, J. Keller and N. Pal of the book “Fuzzy Models and Algorithms for Pattern Recognition and Image Processing”.