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Computational Information Retrieval

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Michael W. Berry, Editor


2001 / xii + 185 pages / Softcover / ISBN: 978-0-898715-00-2 / List Price $62.00 / SIAM Member Price $43.40 / Order Code PR106

Computational Information Retrieval Workshop held in October 2000 in Raleigh, North Carolina.

This volume contains selected papers that focus on the use of linear algebra, computational statistics, and computer science in the development of algorithms and software systems for text retrieval.

Experts in information modeling and retrieval share their perspectives on the design of scalable but precise text retrieval systems, revealing many of the challenges and obstacles that mathematical and statistical models must overcome to be viable for automated text processing. This very useful proceedings is an excellent companion for courses in information retrieval, applied linear algebra, and applied statistics.

Computational Information Retrieval provides background material on vector space models for text retrieval that applied mathematicians, statisticians, and computer scientists may not be familiar with. For graduate students in these areas, several research questions in information modeling are exposed. In addition, several case studies concerning the efficacy of the popular Latent Semantic Analysis (or Indexing) approach are provided.

Part I: Reduced Rank Subspace Models. Lower Dimensional Representation of Text Data in Vector Space Based Information Retrieval, Haesun Park, Moongu Jeon, and J. Ben Rosen; Information Retrieval and Classification with Subspace Representations, Frederick B. Holt and Yuan-Jye Jason Wu; Information Retrieval Using Very Short Krylov Sequences, Katarina Blom and Axel Ruhe; An Incremental Method for Computing Dominant Singular Spaces, Younes Chahlaoui, Kyle A. Gallivan, and Paul Van Dooren; Part II: Probabilistic IR Models and Symbolic Techniques. A Probabilistic Model for Latent Semantic Indexing in Information Retrieval and Filtering, Chris H. Q. Ding; Symbolic Preprocessing Techniques for Information Retrieval Using Vector Space Models, Michael W. Berry, Padma Raghavan, and Xiaoyan Zhang; Part III: Clustering Algorithms and Applications. Detecting Emerging Concepts in Textual Data Mining, William M. Pottenger and Ting-Hao Yang; Clustering Large Unstructured Document Sets, Jacob Kogan; Part IV: Case Studies of Latent Semantic Analysis. Taking a New Look at the Latent Semantic Analysis Approach to Information Retrieval, E. R. Jessup and J. H. Martin; On the Use of the Singular Value Decomposition for Text Retrieval, Parry Husbands, Horst Simon, and Chris H. Q. Ding; Experiments with LSA Scoring: Optimal Rank and Basis, John Caron; A Comparative Analysis of LSI Strategies, Marco Lizza and Flavio Sartoretto; Index.


ISBN: 9780898715002

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