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 court cases of the 3rd overseas convention at the concept 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 rigorously reviewed and chosen from sixty five submissions. The papers disguise subject matters starting from question enlargement, co-occurence research, consumer and interactive modelling, process functionality prediction and comparability, and probabilistic techniques for rating and modelling IR to issues regarding interdisciplinary methods or functions. they're prepared into the subsequent topical sections: predicting question functionality; latent semantic research and note co-occurrence research; question enlargement and re-ranking; comparability of data retrieval structures and approximate seek; likelihood score precept and possible choices; interdisciplinary techniques; person and relevance; outcome 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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A framework for selective query expansion. In: 13th ACM Conference on Information and Knowledge Management, pp. 236–237. ACM Press, New York (2004) 8. : Learning to rank query reformulations. In: 33rd ACM Conference on Research and Development in Information Retrieval, pp. 807–808. 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.

In this situation, we select those terms which maximize the difference between the user and background models. Then, for this subset of the terms, we sort the vocabulary with respect to its collection probability, and then we plot the user probability model for each of the terms in the vocabulary. These figures show how the most ambiguous user obtains a similar distribution to that of the background model, while the distribution of the less ambiguous user is more different. , a ‘5’) is larger for user 2 than for user 1, and at the same time, the most popular rating value (a ‘4’) is much more likely for user 1.

We use three state-of-the-art predictors that were shown above to be specific instances of our framework. The first is a (conceptually) generalized version of the QF method [23]: the overlap at top (nQF ) ranks between the given [k] result list, Lq;QL , and a result list created from the corpus using a relevance [k] model constructed from documents in Lq;QL serves for prediction. Changing the relevance model enables to study the effect of using reference rankings of varying effectiveness. The other two predictors are clarity [6] and NQC [15].

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