Regularizing query-based retrieval scores

Information Retrieval

Volumn 10, Issue 6, 2007, Pages 531-562

  Diaz, Fernando ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Cluster hypothesis; Cluster based retrieval; Document expansion; Pseudo relevance feedback; Query expansion; Regularization

Indexed keywords

EID: 35548956873     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-007-9034-8     Document Type: Article

References (62)

1. Baliński, J., & Daniłowicz, C. (2005). Re-ranking method based on inter-document distances. Information Processing and Management, 41(4), 759-775.
2. Belew, R. K. (1989). Adaptive information retrieval: Using a connectionist representation to retrieve and learn about documents. In SIGIR '89: Proceedings of the 12th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 11-20). New York: ACM Press.
3. Belkin, M., Matveeva, I., & Niyogi, P. (2004). Regularization and semi-supervised learning on large graphs. In COLT (pp. 624-638).
4. Belkin, M., & Niyogi, P. (2003). Using manifold structure for partially labeled classification. In S. T. S. Becker & K. Obermayer (Eds.), Advances in neural information processing systems (Vol. 15, pp. 929-936). Cambridge, MA: MIT Press.
5. Belkin, M., & Niyogi, P. (2004). Semi-supervised learning on Riemannian manifolds. Machine Learning, 56(1-3), 209-239.
6. Belkin, M., & Niyogi, P. (2005). Towards a theoretical foundation for Laplacian-based manifold methods. In COLT (pp. 486-500).
7. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. In WWW7: Proceedings of the Seventh International Conference on World Wide Web 7 (pp. 107-117). Amsterdam: Elsevier Science Publishers B. V.
8. Buckley, C., & Voorhees, E. M. (2000). Evaluating evaluation measure stability. In SIGIR '00: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 33-40). New York: ACM Press.
9. Carmel, D., Yom-Tov, E., Darlow, A., & Pelleg, D. (2006). What makes a query difficult? In SIGIR '06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 390-397). New York: ACM Press.
10. Chen, Z., & Haykin, S. (2002). On different facets of regularization theory. Neural Computation, 14(12), 2791-2846.
11. Chung, F. R. K. (1997). Spectral graph theory. American Mathematical Society.
12. Chung, F. (2004). Laplacians and the Cheeger inequality for directed graphs. Annals of Combinatorics, 9, 1-19.
13. Cohn, D. A., & Hofmann, T. (2000). The missing link - a probabilistic model of document content and hypertext connectivity. In NIPS (pp. 430-436).
14. Connell, M., Feng, A., Kumaran, G., Raghavan, H., Shah, C., & Allan, J. (2004). UMass at TDT 2004. Technical Report CIIR Technical Report IR-357. Department of Computer Science, University of Massachusetts.
15. Croft, W. B., & Lafferty, J. (2003) Language modeling for information retrieval. Kluwer Academic Publishing.
16. Croft, W. B., Lucia, T. J., & Cohen, P. R. (1988). Retrieving documents by plausible inference: A preliminary study. In SIGIR 88: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 481-494). New York: ACM Press.
17. Cronen-Townsend, S., Zhou, Y., & Croft, W. B. (2002). Predicting query performance. In SIGIR '02: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 299-306). New York: ACM Press.
18. Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by latent semantic analysis. Journal of the American Society of Information Science, 41(6), 391-407.
19. Fang, H., Tao, T., & Zhai, C. (2004). A formal study of information retrieval heuristics. In SIGIR '04: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 49-56). New York: ACM Press.
20. Fang, H., & Zhai, C. (2005) An exploration of axiomatic approaches to information retrieval. In SIGIR '05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 480-487). New York: ACM Press.
21. Harman, D. K. (1993). The first text retrieval conference (TREC-1) Rockville, MD, U.S.A., 4-6 November, 1992. Information Processing and Management, 29(4), 411-414.
22. Jardine, N., & van Rijsbergen, C. J. (1971). The use of hierarchic clustering in information retrieval. Information Storage and Retrieval, 7, 217-240.
23. Kleinberg, J. M. (1998). Authoritative sources in a hyperlinked environment. In SODA '98: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 668-677). Philadelphia: Society for Industrial and Applied Mathematics.
24. Krovetz, R. (1993). Viewing morphology as an inference process. In SIGIR '93: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 191-202). New York: ACM Press.
25. Kurland, O., & Lee, L. (2004). Corpus structure, language models, and ad hoc information retrieval. In SIGIR '04: Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval (pp. 194-201). New York: ACM Press.
26. Kurland, O., & Lee, L. (2005) PageRank without hyperlinks: Structural re-ranking using links induced by language models. In SIGIR '05: Proceedings of the 28th Annual International Conference on Research and Development in Information Retrieval.
27. Kurland, O., Lee, L., & Domshlak, C. (2005). Better than the real thing? Iterative pseudo-query processing using cluster-based language models. In SIGIR '05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 19-26). New York: ACM Press.
28. Kwok, K. L. (1989). A neural network for probabilistic information retrieval. In SIGIR '89: Proceedings of the 12th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 21-30). New York: ACM Press.
29. Lafferty, J., & Lebanon, G. (2005). Diffusion kernels on statistical manifolds. Journal of Machine Learning Research, 6, 129-163.
30. Lafon, S. (2004). Diffusion maps and geometric harmonics. PhD Thesis, Yale University.
31. Lavrenko, V. (2004). A generative theory of relevance. Ph.D. Thesis, University of Massachusetts.
32. Lavrenko, V., & Allan, J. (2006). Real-time query expansion in relevance models. Technical Report IR-473. Amherst: University of Massachusetts.
33. Liu, X., & Croft, W. B. (2004). Cluster-based retrieval using language models. In SIGIR '04: Proceedings of the 27th Annual International Conference on Research and Development in Information Retrieval (pp. 186-193). New York: ACM Press.
34. McCallum, A. K. (1996). Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/mccallum/ bow.
35. Metzler, D., & Croft, W. B. (2004). Combining the language model and inference network approaches to retrieval. Information Processing and Management, 40(5), 735-750.
36. Metzler, D., & Croft, W. B. (2005). A Markov random field model for term dependencies. In SIGIR '05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 472-479). New York: ACM Press.
37. Montague, M., & Aslam, J. A. (2001). Relevance score normalization for metasearch. In CIKM '01: Proceedings of the Tenth International Conference on Information and Knowledge Management (pp. 427-433). New York: ACM Press.
38. Ng, A. Y., Zheng, A. X., & Jordan, M. I. (2001). Stable algorithms for link analysis. In SIGIR '01: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 258-266). New York: ACM Press.
39. Petersen, K. B., & Pedersen, M. S. (2005) The matrix Cookbook. Version 20051003.
40. Platt, J. (2000). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In P. J. Bartlett, B. Schölkopf, D. Schuurmans, & A. J. Smola (Eds.), Advances in large margin classifiers. MIT Press
41. Qin, T., Liu, T.-Y., Zhang, X.-D., Chen, Z., & Ma, W.-Y. (2005). A study of relevance propagation for web search. In SIGIR '05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 408-415). New York: ACM Press.
42. Richardson, M., Prakash, A., & Brill, E. (2006). Beyond PageRank: Machine learning for static ranking. In WWW '06: Proceedings of the 15th International Conference on World Wide Web (pp. 707-715). New York: ACM Press.
43. Robertson, S. E., van Rijsbergen, C. J., & Porter, M. F. (1981). Probabilistic models of indexing and searching. In SIGIR '80: Proceedings of the 3rd Annual ACM Conference on Research and Development in Information Retrieval (pp. 35-56). Kent: Butterworth & Co.
44. Robertson, S. E., & Walker, S. (1994). Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In SIGIR '94: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 232-241). New York: Springer-Verlag Inc.
45. Rocchio, J. J. (1971). Relevance feedback in information retrieval. In The SMART retrieval system: Experiments in automatic document processing (pp. 313-323). Prentice-Hall Inc.
46. Rölleke, T., Tsikrika, T., & Kazai, G. (2006). A general matrix framework for modelling information retrieval. Information Processing and Management, 42(1), 4-30.
47. Salton, G. (1968). Automatic information organization and retrieval. McGraw Hill Text.
48. Salton, G., & Buckley, C. (1988). On the use of spreading activation methods in automatic information. In SIGIR '88: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 147-160). New York: ACM Press.
49. Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613-620.
50. Savoy, J. (1997). Ranking schemes in hybrid Boolean systems: A new approach. Journal of the American Society for Information Science, 48(3), 235-253.
51. Singhal, A., & Pereira, F. (1999). Document expansion for speech retrieval. In SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 34-41). New York: ACM Press.
52. Strohman, T., Metzler, D., Turtle, H., & Croft, W. B. (2004). Indri: A language model-based search engine for complex queries. In Proceedings of the International Conference on Intelligence Analysis.
53. Tao, T., Wang, X., Mei, Q., & Zhai, C. (2006). Language model information retrieval with document expansion. In HLT/NAACL 2006 (pp. 407-414).
54. Turtle, H., & Croft, W. B. (1990). Inference networks for document retrieval. In SIGIR '90: Proceedings of the 13th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1-24). New York: ACM Press.
55. Voorhees, E. (2004). Overview of the TREC 2004 Robust Track. In Proceedings of the 13th Text REtrieval Conference (TREC 2004).
56. Voorhees, E. M., & Harman, D. K. (Eds.). (2001). TREC: Experiment and evaluation in information retrieval. MIT Press.
57. Wei, X., & Croft, W. B. (2006). LDA-based document models for ad-hoc retrieval. In SIGIR '06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 178-185). New York: ACM Press.
58. Wilkinson, R., & Hingston, P. (1991). Using the cosine measure in a neural network for document retrieval. In SIGIR '91: Proceedings of the 14th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 202-210). New York: ACM Press.
59. Xu, J., & Croft, W. B. (1999). Cluster-based language models for distributed retrieval. In SIGIR '99: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 254-261). New York: ACM Press.
60. Yom-Tov, E., Fine, S., Carmel, D., & Darlow, A. (2005). Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval. In SIGIR '05: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 512-519). New York: ACM Press.
61. Zhou, D., Schölkopf, B., & Hofmann, T. (2005). Semi-supervised learning on directed graphs. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems (Vol. 17, pp. 1633-1640). Cambridge, MA: MIT Press.
62. Zhou, D., Weston, J., Gretton, A., Bousquet, O., & Schölkopf, B. (2004). Ranking on data manifolds. In L. S. Thrun & B. Schölkopf (Eds.), Advances in neural information processing systems (Vol. 16, pp. 169-176). Cambridge, MA: MIT Press.