A query model based on normalized log-likelihood

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2009, Pages 1903-1906

  Meij, Edgar ^a   Weerkamp, Wouter ^a   De Rijke, Maarten ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

Language modeling; Query models; Relevance feedback

Indexed keywords

LANGUAGE MODELING; LOG LIKELIHOOD; QUERY MODEL; RELEVANCE ASSESSMENTS; RELEVANCE FEEDBACK; RELEVANT DOCUMENTS;

COMPUTATIONAL LINGUISTICS; KNOWLEDGE MANAGEMENT; STATE FEEDBACK;

FEEDBACK;

EID: 74549137318     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646261     Document Type: Conference Paper

References (23)

1. J. Bai and J.-Y. Nie. Adapting information retrieval to query contexts. IPM, 44(6):1901-1922, 2008.
2. K. Balog, W. Weerkamp, and M. de Rijke. A few examples go a long way: constructing query models from elaborate query formulations. In SIGIR '08, 2008.
3. M. Bendersky and B. W. Croft. Discovering key concepts in verbose queries. In SIGIR '08, 2008.
4. C. Buckley and S. Robertson. Relevance feedback track overview: TREC 2008. In TREC '08, 2008.
5. C. Buckley, D. Dimmick, I. Soboroff, and E. Voorhees. Bias and the limits of pooling for large collections. Information Retrieval, 10(6):491-508, 2007.
6. M. Clements, A. de Vries, and M. Reinders. The influence of personalization on tag query length in social media search. Information Processing & Management, In Press, Corrected Proof:-, 2009.
7. P. Clough, H. Müller, T. Deselaers, M. Grubinger, T. Lehmann, J. Jensen, and W. Hersh. The CLEF 2005 Cross-Language Image Retrieval Track. In CLEF 2005 Working Notes, 2005.
8. D. Hiemstra. A linguistically motivated probabilistic model of information retrieval. In ECDL '98, 1998.
9. O. Kurland. The opposite of smoothing: a language model approach to ranking query-specific document clusters. In SIGIR '08, 2008.
10. J. Lafferty and C. Zhai. Document language models, query models, and risk minimization for information retrieval. In SIGIR '01, 2001.
11. V. Lavrenko and B. W. Croft. Relevance models in information retrieval. In B. W. Croft and J. Lafferty, editors, Language Modeling for Information Retrieval, pages 11-54. Kluwer, 2003.
12. D. Losada and L. Azzopardi. An analysis on document length retrieval trends in language modeling smoothing. Information Retrieval, 11(2):109-138, 2008.
13. E. Meij, W. Weerkamp, K. Balog, and M. de Rijke. Parsimonious relevance models. In SIGIR '08, 2008.
14. D. Metzler and B. W. Croft. A markov random field model for term dependencies. In SIGIR '05, 2005.
15. K. Ng. A maximum likelihood ratio information retrieval model. In TREC 2000, 2000.
16. J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice Hall, 1971.
17. G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. JASIST, 41(4): 288-297, 1990.
18. A. Spink, B. J. Jansen, and C. H. Ozmultu. Use of query reformulation and relevance feedback by excite users. Internet Research: Electronic Networking Applications and Policy, 10(4):317-328, 2000.
19. A. Spink, B. J. Jansen, D. Wolfram, and T. Saracevic. From e-sex to e-commerce: Web search changes. IEEE Computer, 35(3):107-109, 2002.
20. X. Wang and C. Zhai. Mining term association patterns from search logs for effective query reformulation. In CIKM '08, 2008.
21. W. Weerkamp, K. Balog, and E. J. Meij. A generative language modeling approach for ranking entities. In Advances in Focused Retrieval, 2009.
22. C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In CIKM '01, 2001.
23. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM TOIS, 22(2):179-214, 2004.