Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

To improve the precision at the very top ranks of a document list presented in response to a query, researchers suggested to exploit information induced from clustering of documents highly ranked by some initial search. We propose a novel model for ranking such (query-specific) clusters by the presumed percentage of relevant documents that they contain. The model is based on (i) proposing a palette of "witness" cluster properties that purportedly correlate with this percentage, (ii) devising concrete quantitative measures for these properties, and (iii) ordering the clusters via aggregation of rankings induced by these individual measures. Empirical evaluation shows that our model is consistently more effective than previously suggested methods in detecting clusters containing a high relevant-document percentage. Furthermore, the precision-at-top-ranks performance of this model transcends that of standard document-based retrieval, and competes with that of a state-of-the-art document-based retrieval approach.

REFERENCES

• 1: N. Abdul-Jaleel, J. Allan, W. B. Croft, F. Diaz, L. Larkey, X. Li, M. D. Smucker, and C. Wade. UMASS at TREC 2004 - novelty and hard. In Proceedings of TREC-13, 2004.
• 2: C. Buckley, G. Salton, J. Allan, and A. Singhal. Automatic query expansion using SMART: TREC3. In Proceedings of TREC-3, pages 69--80, 1994.
• 3: Kevyn Collins-Thompson , Jamie Callan, Estimation and use of uncertainty in pseudo-relevance feedback, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277795]
• 4: W. B. Croft. A model of cluster searching based on classification. Information Systems, 5:189--195, 1980.
• 5: W. Bruce Croft , John Lafferty, Language Modeling for Information Retrieval, Kluwer Academic Publishers, Norwell, MA, 2003
• 6: P. Diaconis. Group Theory in Statistics. Harvard Lecture Notes, 1982.
• 7: Fernando Diaz, Regularizing ad hoc retrieval scores, Proceedings of the 14th ACM international conference on Information and knowledge management, October 31-November 05, 2005, Bremen, Germany [doi>10.1145/1099554.1099722]
• 8: Fernando Diaz , Donald Metzler, Improving the estimation of relevance models using large external corpora, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148200]
• 9: Cynthia Dwork , Ravi Kumar , Moni Naor , D. Sivakumar, Rank aggregation methods for the Web, Proceedings of the 10th international conference on World Wide Web, p.613-622, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372165]
• 10: E. A. Fox and J. A. Shaw. Combination of multiple searches. In Proceedings of TREC-2, 1994.
• 11: Marti A. Hearst , Jan O. Pedersen, Reexamining the cluster hypothesis: scatter/gather on retrieval results, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.76-84, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243216]
• 12: N. Jardine and C. J. van Rijsbergen. The use of hierarchic clustering in information retrieval. Information Storage and Retrieval, 7(5):217--240, 1971.
• 13: J. Kleinberg. Authoritative sources in a hyperlinked environment. Technical Report Research Report RJ 10076, IBM, May 1997.
• 14: Oren Kurland , Lillian Lee, Inter-document similarities, language models, and ad hoc information retrieval, Cornell University, Ithaca, NY, 2006
• 15: Oren Kurland, The opposite of smoothing: a language model approach to ranking query-specific document clusters, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390366]
• 16: Oren Kurland , Lillian Lee, Corpus structure, language models, and ad hoc information retrieval, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009027]
• 17: Oren Kurland , Lillian Lee, PageRank without hyperlinks: structural re-ranking using links induced by language models, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076087]
• 18: Oren Kurland , Lillian Lee, Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148188]
• 19: John Lafferty , Chengxiang Zhai, Document language models, query models, and risk minimization for information retrieval, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.111-119, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383970]
• 20: Victor Lavrenko , W. Bruce Croft, Relevance based language models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.120-127, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383972]
• 21: V. Lavrenko and W. B. Croft. Relevance models in information retrieval. In Croft and Lafferty {5}, pages 11--56.
• 22: Anton Leuski, Evaluating document clustering for interactive information retrieval, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502592]
• 23: Anton Leouski , James Allan, Evaluating a Visual Navigation System for a Digital Library, Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries, p.535-554, September 21-23, 1998
• 24: Xiaoyong Liu , W. Bruce Croft, Cluster-based retrieval using language models, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009026]
• 25: X. Liu and W. B. Croft. Experiments on retrieval of optimal clusters. Technical Report IR-478, Center for Intelligent Information Retrieval (CIIR), University of Massachusetts, 2006.
• 26: Xiaoyong Liu , W. Bruce Croft, Representing clusters for retrieval, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148310]
• 27: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 28: S. E. Preece. Clustering as an output option. In Proceedings of the American Society for Information Science, pages 189--190, 1973.
• 29: Ian Ruthven , Mounia Lalmas, A survey on the use of relevance feedback for information access systems, The Knowledge Engineering Review, v.18 n.2, p.95-145, June 2003 [doi>10.1017/S0269888903000638]
• 30: J. G. Shanahan, J. Bennett, D. A. Evans, D. A. Hull, and J. Montgomery. Clairvoyance Corporation experiments in the TREC 2003. High accuracy retrieval from documents (HARD) track. In Proceedings of TREC-12, pages 152--160, 2003.
• 31: Anastasios Tombros , Robert Villa , C. J. Van Rijsbergen, The effectiveness of query-specific hierarchic clustering in information retrieval, Information Processing and Management: an International Journal, v.38 n.4, p.559-582, July 2002 [doi>10.1016/S0306-4573(01)00048-6]
• 32: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 33: Ellen M. Voorhees , Donna K. Harman, TREC: Experiment and Evaluation in Information Retrieval (Digital Libraries and Electronic Publishing), The MIT Press, 2005
• 34: P. Willett. Query specific automatic document classification. International Forum on Information and Documentation, 10(2):28--32, 1985.
• 35: Jinxi Xu , W. Bruce Croft, Query expansion using local and global document analysis, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.4-11, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243202]
• 36: Elad Yom-Tov , Shai Fine , David Carmel , Adam Darlow, Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076121]
• 37: H. P. Young. An axiomatization of Borda's rule. Journal of Economic Theory, 9:43--52, 1974.
• 38: Oren Zamir , Oren Etzioni, Web document clustering: a feasibility demonstration, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.46-54, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290956]
• 39: Chengxiang Zhai , John Lafferty, Model-based feedback in the language modeling approach to information retrieval, Proceedings of the tenth international conference on Information and knowledge management, October 05-10, 2001, Atlanta, Georgia, USA [doi>10.1145/502585.502654]
• 40: Chengxiang Zhai , John Lafferty, A study of smoothing methods for language models applied to Ad Hoc information retrieval, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.334-342, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384019]
• 41: H. J. Zimmermann. Fuzzy Set Theory. Kluwer Academic, 3 edition, 1996.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms, Experimentation

Keywords:
cluster properties, clusters, language models, optimal cluster, query-specific clustering, rank aggregation