Parameterized concept weighting in verbose queries

SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2011, Pages 605-614

  Bendersky, Michael ^a   Metzler, Donald ^b   Croft, W Bruce ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Parameterized concept weighting; Query expansion

Indexed keywords

INFORMATION RETRIEVAL;

IMPORTANCE WEIGHTS; INFORMATION RETRIEVAL MODELS; PARAMETERIZED; PSEUDO RELEVANCE FEEDBACK; QUERY EXPANSION; RANKING METHODS; RETRIEVAL MODELS; STATE OF THE ART;

PARAMETERIZATION;

EID: 80052122147     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2009916.2009998     Document Type: Conference Paper

References (39)

1. J. Allan, J. Aslam, B. Carterette, V. Pavlu, and E. Kanoulas. Million Query Track 2008 overview. In Proc. of TREC, 2008.
2. J. Allan, M. E. Connell, W. B. Croft, F. F. Feng, D. Fisher, and X. Li. INQUERY and TREC-9. In Proc. of TREC-9, pages 551-562, 2000.
3. G. Amati and C. J. Van Rijsbergen. Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems (TOIS), 20(4):357-389, October 2002.
4. N. Balasubramanian, G. Kumaran, and V. R. Carvalho. Exploring reductions for long web queries. In Proc. of SIGIR, pages 571-578, 2010.
5. M. Bendersky and W. B. Croft. Discovering key concepts in verbose queries. In Proc. of SIGIR, pages 491-498, 2008.
6. M. Bendersky, D. Metzler, and W. B. Croft. Learning concept importance using a weighted dependence model. In Proc. of WSDM, pages 31-40, 2010.
7. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proc. of ICML, pages 89-96, 2005. (Pubitemid 43183320)
8. G. Cao, J.-Y. Nie, J. Gao, and S. Robertson. Selecting good expansion terms for pseudo-relevance feedback. In Proc. of SIGIR, pages 243-250, 2008.
9. J. Fagan. Automatic phrase indexing for document retrieval. In Proc. of SIGIR, pages 91-101, 1987.
10. J. Gao, H. Qi, X. Xia, and J.-Y. Nie. Linear discriminant model for information retrieval. In Proc. of SIGIR, pages 290-297, 2005.
11. D. Hiemstra. Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term. In In Proc. of SIGIR, pages 35-41, 2002.
12. S. Huston and W. B. Croft. Evaluating verbose query processing techniques. In Proc. of SIGIR, pages 291-298, 2010.
13. T. Joachims. Optimizing search engines using clickthrough data. In Proc. of KDD, pages 133-142, 2002.
14. G. Kumaran and V. R. Carvalho. Reducing long queries using query quality predictors. In Proc. of SIGIR, pages 564-571, 2009.
15. H. Lang, D. Metzler, B. Wang, and J.-T. Li. Improved latent concept expansion using hierarchical markov random fields. In Proc. of CIKM, pages 249-258, 2010.
16. V. Lavrenko and W. B. Croft. Relevance Models in Information Retrieval. In W. B. Croft and J. Lafferty, editors, Language modeling for Information Retrieval, pages 11-56. Kluwer, 2003.
17. M. Lease. Incorporating relevance and pseudo-relevance feedback in the markov random field model. In Proc. of TREC, 2008.
18. M. Lease. An improved markov random field model for supporting verbose queries. In Proc. of SIGIR, pages 476-483, 2009.
19. T.-Y. Liu. Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval, 3(3), 2009.
20. H. Luhn. The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development, 2(2):159, 1958.
21. Y. Lv and C. Zhai. Positional relevance model for pseudo-relevance feedback. In Proc. of SIGIR, pages 579-586, 2010.
22. C. Macdonald and I. Ounis. Global statistics in proximity weighting models. In Proc. of the SIGIR Web N-Gram Workshop, 2010.
23. D. Metzler and W. B. Croft. A Markov random field model for term dependencies. Proc. of SIGIR, pages 472-479, 2005.
24. D. Metzler and W. B. Croft. Latent concept expansion using markov random fields. Proc. of SIGIR, pages 311-318, 2007. (Pubitemid 350164975)
25. D. Metzler and W. B. Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3):257-274, 2007.
26. D. Metzler, T. Strohman, and W. B. Croft. Indri at TREC 2005: Terabyte track. In Proc. of TREC, 2005.
27. G. Mishne and M. de Rijke. Boosting Web Retrieval Through Query Operations. In Proc. of ECIR, pages 502-516, 2005. (Pubitemid 41272928)
28. J. Peng, C. Macdonald, B. He, V. Plachouras, and I. Ounis. Incorporating term dependency in the DFR framework. In Proc. of SIGIR, pages 843-844, 2007. (Pubitemid 350165098)
29. J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proc. of SIGIR, pages 275-281, 1998.
30. S. E. Robertson and S. Walker. Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In Proc. of SIGIR, pages 232-241, 1994.
31. L. Shi and J.-Y. Nie. Using various term dependencies according to their utilities. In Proc. of CIKM, pages 1493-1496, 2010.
32. M. D. Smucker, C. Clarke, and G. V. Cormack. Experiments with ClueWeb09: Relevance feedback and web tracks. In Proc. of TREC, 2009.
33. R. Song, M. Taylor, J.-R. Wen, H.-W. Hon, and Y. Yu. Viewing term proximity from a different perspective. In Proc. of ECIR, pages 346-357, 2008. (Pubitemid 351499081)
34. T. Strohman, D. Metzler, H. Turtle, and W. B. Croft. Indri: A language model-based search engine for complex queries. In Proc. of IA, 2004.
35. K. M. Svore, P. H. Kanani, and N. Khan. How good is a span of terms?: exploiting proximity to improve web retrieval. In Proc. of SIGIR, pages 154-161, 2010.
36. T. Tao and C. Zhai. An exploration of proximity measures in information retrieval. In Proc. of SIGIR, pages 295-302, 2007. (Pubitemid 350164973)
37. L. Wang, D. Metzler, and J. Lin. Ranking under temporal constraints. In Proc. of CIKM, pages 79-88, 2010.
38. J. Xu and W. B. Croft. Query expansion using local and global document analysis. Proc. of SIGIR, pages 4-11, 1996.
39. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems, 22(2):179-214, 2004.