Download Advances in Information Retrieval Theory: Third by ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani PDF
By ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)
This booklet constitutes the refereed complaints of the 3rd foreign convention at the conception of knowledge Retrieval, ICTIR 2011, held in Bertinoro, Italy, in September 2011. The 25 revised complete papers and thirteen brief papers awarded including the abstracts of 2 invited talks have been conscientiously reviewed and chosen from sixty five submissions. The papers disguise issues starting from question growth, co-occurence research, consumer and interactive modelling, approach functionality prediction and comparability, and probabilistic ways for rating and modelling IR to subject matters on the topic of interdisciplinary techniques or functions. they're prepared into the next topical sections: predicting question functionality; latent semantic research and observe co-occurrence research; question growth and re-ranking; comparability of knowledge retrieval platforms and approximate seek; likelihood score precept and choices; interdisciplinary methods; consumer and relevance; consequence diversification and question disambiguation; and logical operators and descriptive approaches.
Read or Download Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings PDF
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Extra resources for Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings
All predictors discussed above are based on a single retrieval (if at all) used to create a pseudo (in)eﬀective ranking. Alternatively, fusion of multiple rankings can be used to produce a pseudo eﬀective ranking . Indeed, the merits of fusion, in terms of retrieval eﬀectiveness, have been acknowledged . Pearson’s correlation between the given result list and that produced by fusion served for query-performance prediction . Clearly, this prediction approach is a speciﬁc instance of our framework (with α(q) = 1 and β(q) = 0).
ACM Press, New York (2010) 9. : Predicting the Effectiveness of Queries and Retrieval Systems. PhD thesis, University of Twente, Enschede (2010) 10. : A Survey of Pre-Retrieval Query Performance Predictors. In: 17th ACM Conference on Information and Knowledge Management, pp. 439– 448.
301–312. Springer, Heidelberg (2009) 11. : Inferring query performance using pre-retrieval predictors. , Melucci, M. ) SPIRE 2004. LNCS, vol. 3246, pp. 43–54. Springer, Heidelberg (2004) 12. : Relevance-based language models. In: Proceedings of SIGIR, pp. 120–127 (2001) 13. : The probability ranking principle in IR. Journal of Documentation, 294–304 (1977) 14. : Relevance feedback in information retrieval. In: Salton, G. ) The SMART Retrieval System: Experiments in Automatic Document Processing, pp.