Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang's Advances in Information Retrieval: 36th European Conference PDF

By Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann

ISBN-10: 3319060279

ISBN-13: 9783319060279

ISBN-10: 3319060287

ISBN-13: 9783319060286

This publication constitutes the lawsuits of the thirty sixth eu convention on IR examine, ECIR 2014, held in Amsterdam, The Netherlands, in April 2014.

The 33 complete papers, 50 poster papers and 15 demonstrations offered during this quantity have been conscientiously reviewed and chosen from 288 submissions. The papers are equipped within the following topical sections: assessment, suggestion, optimization, semantics, aggregation, queries, mining social media, electronic libraries, potency, and data retrieval idea. additionally incorporated are three educational and four workshop presentations.

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Read or Download Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings PDF

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Additional resources for Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings

Example text

Jk,1 ... Jk,t(k) the higher its weight should be. We require that the p system weights be bounded to [0, 1] and that they always sum to 1, (||wi ||1 = 1); thus they represent a distribution. , w1p } to the uniform distribution, as 1/p, and then performing the first E-step. , wtp }. We then compute the pseudo-judgment Jm,n for each document Dm,n as: p t = Jm,n ∑ wtj · f (VS j ,Dm,n ) . (1) j=1 The score transformation function f (·) allows us to implement a range of estimators for the E-stage by transforming the system’s retrieval status value VS j ,Dm,n prior to performing the weighted linear combination across systems.

Wilkie and L. Azzopardi To summarise, we have described a range of models that are related but make different assumptions about how to model a term’s relevance. Most models are composed of two main parts: one to estimate the value of the information content of the term, and one to regulate the influence of the document’s length. Overly focusing on one part or another, or ignoring one part invariably leads to some form of bias creeping into the retrieval model. 3 Experimental Method The focus of this study is to assess the level of bias exhibited by each retrieval model/weighting, and then to determine whether there is any relationship between the level of bias and retrieval performance.

Probabilistic models in ir. Computer Journal 35(3), 243–255 (1992) 10. : The estimation of the lorenz curve and gini index. The Review of Economics and Statistics 54, 306–316 (1972) 11. : A probabilistic approach to automatic keyword indexing. part i. on the distribution of specialty words in a technical literature. Journal of the American Society for Information Science 26(4), 197–206 (1975) 12. idf term weighting in information retrieval. International Journal on Digital Libraries 3(2), 131–139 (2000) 13.

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Advances in Information Retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings by Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofmann


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