New PDF release: Advances in Information Retrieval Theory: Third

By ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)

ISBN-10: 364223318X

ISBN-13: 9783642233180

This ebook constitutes the refereed lawsuits of the 3rd overseas convention at the idea of knowledge Retrieval, ICTIR 2011, held in Bertinoro, Italy, in September 2011. The 25 revised complete papers and thirteen brief papers provided including the abstracts of 2 invited talks have been conscientiously reviewed and chosen from sixty five submissions. The papers hide issues starting from question growth, co-occurence research, person and interactive modelling, approach functionality prediction and comparability, and probabilistic methods for score and modelling IR to subject matters concerning interdisciplinary techniques or functions. they're geared up into the next topical sections: predicting question functionality; latent semantic research and notice co-occurrence research; question growth and re-ranking; comparability of data retrieval platforms and approximate seek; chance rating precept and choices; interdisciplinary techniques; person and relevance; consequence diversification and question disambiguation; and logical operators and descriptive approaches.

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Extra resources for Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings

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Let p(·|L) denote a language model induced from the document list L; and, let def Sim(L1 , L2 ) = −KL p(·|L1 )||p(·|L2 ) be an inter-list similarity measure that is based on the KL divergence between the lists’ language models. Indeed, the [k] [k] [k] [k] clarity of LM is defined as −Sim(LM , LP IE ); p(·|LM ) is a relevance language [k] [k] model [12] induced from LM ; and, p(·|LP IE ) is the corpus language model, as [k] the corpus is the only (pseudo) document that appears (k times) in LP IE .

In: 17th ACM Conference on Information and Knowledge Management, pp. 439– 448.

Other examples such as statistic approaches based on Inverse Document Frequency (IDF), and variations thereof, have also been proposed [11, 16]. He & Ounis [11] propose a predictor based on the standard deviation of the IDF of the query terms. Plachouras et al. [16] represent the quality of a query term by a modification of IDF, where instead of the number of documents, the number of words in the whole collection is used, and the query length acts as a normalizing factor. These IDF-based predictors obtained moderate correlation with respect the query performance.

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Advances in Information Retrieval Theory: Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011. Proceedings by ChengXiang Zhai (auth.), Giambattista Amati, Fabio Crestani (eds.)


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