Abstract
Although a technique of relevance feedback is common in the field of information retrieval (IR), the feedback is usually done by means of query refinement; restructuring of the information space has not been attempted yet. The restructuring not only allows useful applications such as clustering but also is indispensable for IR if a modeling function employs correlation of terms. In this paper we present a new method of relevance feedback through the restructuring of the information space. Our method adapts document space to the user’s mental model by manipulating a dictionary vector. Therefore, user’s viewpoint is preserved after a series of retrieval processes and reused for retrieval performed later. We show its effectiveness through the retrieval experiments on FAQ (Frequntly Asked Questions) documents.
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Salton, G., Allan, J. and Buckley, C., “Automatic Structuring and Retrieval of Large Text Files,”Communications of the ACM. 37, 2, pp. 97–108, 1994.
Salton, G. and Buckley, C., “Term-Weighting Approaches In Automatic Text retrieval,”Information Processing & Management. 24, 5, pp. 515–523, 1988.
Rocchio, J. J., “Relevance Feedback in Information Retrieval,”The Smart Retrieval System- Experiments in Automatic Document Processing, Prentice-Hall, Inc., pp. 313–323, 1971.
Buckley, C. and Salton, G., “Optimization of Relevance Feedback Weights,”Proce. of the 18th Annual International ACM SIGIR Conference on research and Development in Information Retrieval, pp. 351–357, 1995.
Robertson, S. E. and Jones, K. S., “Relevance Weighting of Search Terms,”the American Society for Information Science. 27, 3, pp. 129–146, 1976.
Harman, D., “Relevance Feedback Revisited,”Proce. of ACM SIGIR 1992, International Conference on Research and Development in Information Rerieval, pp. 1–10, 1992.
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Tomoko Murakami: She obtained her bachelor’s degree in Engineering from Aoyama Gakuin University in 1996, and her master’s degree in Media and Governance from Keio University in 1998. In 1998 she joined Human Interface Labolatory, Corporate Research & Development Center, Toshiba Corporation, Kawasaki, Japan. Her research interests are in Machine Learning, especially Inductive Logic Programming. She is a member of JSAI.
Ryohei Orihara, Ph.D.: He is a research scientist at Human Interface Laboratory, Corporate Research & Development Center, Toshiba Corporation. He obtained his bachelor’s degree and master’s degree in Engineering and Ph.D. from University of Tsukuba in 1986, 1988 and 1999 respectively. His current research interests include machine learning, creativity support system, analogical reasoning and metaphor understanding. He was a visiting researcher at University of Toronto from 1993 to 1995. He is a member of IPSJ, JSAI and JSSST. He is presently on the editorial committee of the Journal of JSAI.
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Murakami, T., Orihara, R. Friendly information retrieval through adaptive restructuring of information space. New Gener Comput 18, 137–146 (2000). https://doi.org/10.1007/BF03037592
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DOI: https://doi.org/10.1007/BF03037592