Enhancing the Set-Based Model Using Proximity Information
(SBM), which is an effective technique for computing term weights based on co-occurrence patterns, employing the information about the proximity among query terms in documents. The intuition that semantically related term occurrences often occur closer to each other is taken into consideration, leading to a new information retrieval model called proximity set-based model (PSBM). The novelty is that the proximity information is used as a pruning strategy to determine only related co-occurrence term patterns. This technique is time efficient and yet yields nice improvements in retrieval effectiveness. Experimental results show that PSBM improves the average precision of the answer set for all four collections evaluated. For the CFC collection, PSBM leads to a gain relative to the standard vector space model (VSM), of 23% in average precision values and 55% in average precision for the top 10 documents. PSBM is also competitive in terms of computational performance, reducing the execution time of the SBM in 21% for the CISI collection.
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- R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference Management of Data, pages 207–216, Washington, D.C., May 1993.Google Scholar
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In The 20th International Conference on Very Large Data Bases, pages 487–499, Santiago, Chile, September 1994.Google Scholar
- J. P. Callan. Passage-level evidence in document retrieval. In Proceedings of the 17th ACM SIGIR Conference on Research and Development in Information Retrieval, pages 302–310, Dublin, Ireland, 1994.Google Scholar
- C. L. A. Clarke, G.V. Cormack, and F. J. Burkowski. Shortest substring ranking. In In Fourth Text REtrieval Conference (TREC-4), pages 295–304, Gaithersburg, Maryland, USA, November 1995.Google Scholar
- E. Fox. Characterization of two new experimental collections in computer and information science containing textual and bibliographical concepts. Technical report, 1983. http://www.ncstrl.org.
- D. Hawking and P. Thistlewaite. Proximity operators-so near and yet so far. In In Fourth Text REtrieval Conference (TREC-4), pages 131–144, Gaithersburg, Maryland, USA, November 1995.Google Scholar
- M. Kaszkeil and J. Zobel. Passage retrieval revisited. In Proceedings of the 20th ACM SIGIR Conference on Research and Development in Information Retrieval, pages 178–185, Philadelphia, Philadelphia, USA, 1997.Google Scholar
- D. Knaus, E. Mittendorf, P. Schauble, and P. Sheridan. Highlighting relevant passages for users of the interactive spider retrieval system. In Proceedings of the Fourth Text REtrieval Conference (TREC-4), pages 233–244, Gaithersburg, Maryland, USA, 1996.Google Scholar
- E. Mittendorf and P. Schauble. Document and passage retrieval based on hidden markov models. In Proceedings of the 17th ACM SIGIR Conference on Research and Development in Information Retrieval, pages 318–327, Dublin, Ireland, 1994.Google Scholar
- M. Persin, J. Zobel, and R. Sacks-Davis. Filtered document retrieval with frequency-sorted indexes. In Journal of the American Society of Information Science, pages 749–764, 1996.Google Scholar
- B. Pôssas, N. Ziviani, W. Meira, and B. Ribeiro-Neto. Set-based model: A new approach for information retrieval. In The 25th ACM-SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, August 2002.Google Scholar
- G. Salton, J. Allan, and C. Buckley. Approaches to passage retrieval in full text information systems. In Proceedings of the 16th ACM SIGIR Conference on Research and Development in Information Retrieval, pages 49–58, Pittsburgh, Philadelphia, USA, 1993.Google Scholar
- W.M. Shaw, J.B. Wood, R.E. Wood, and H.R. Tibbo. The cystic fibrosis database: Content and research opportunities. In Library and Information Science Research, volume 13), pages 347–366, 1991.Google Scholar
- I. Witten, A. Moffat, and T. Bell. Managing Gigabytes. Compressing and Indexing Documents and Images. Morgan Kaufmann Publishers, 1999.Google Scholar
- S.K.M. Wong, W. Ziarko, V.V. Raghavan, and P.C.N. Wong. On modeling of information retrieval concepts in vector spaces. In The ACM Transactions on Databases Systems, volume 12(2), pages 299–321, June 1987.Google Scholar
- S.K.M. Wong, W. Ziarko, and P.C.N. Wong. Generalized vector space model in information retrieval. In The 8th ACM-SIGIR Conference on Research and Development in Information Retrieval, pages 18–25, New York, USA, 1985.Google Scholar
- C. T. Yu and G. Salton. Precision weighting-an effective automatic indexing method. In Journal of the ACM, volume 23(1), pages 76–88, January 1976.Google Scholar
- M. J. Zaki. Generating non-redundant association rules. In 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 34–43, Boston, MA, USA, August 2000.Google Scholar