Advances in Information Retrieval: 33rd European Conference by Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal

By Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee, Vanessa Mudoch (eds.)

This publication constitutes the refereed court cases of the thirty third annual ecu convention on details Retrieval learn, ECIR 2011, held in Dublin, eire, in April 2010. The forty five revised complete papers provided including 24 poster papers, 17 brief papers, and six software demonstrations have been conscientiously reviewed and chosen from 223 complete learn paper submissions and sixty four poster/demo submissions. The papers are equipped in topical sections on textual content categorization, recommender structures, net IR, IR overview, IR for Social Networks, cross-language IR, IR thought, multimedia IR, IR functions, interactive IR, and query answering /NLP.

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Extra info for Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings

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Graphical representations of Folk-LDA (left), MM-LDA (middle) and FolkMM-LDA (right) Table 1. The Generative process for Folk-LDA: K is the number of underlying latent topics; T represents the tag vocabulary after expansion For each document d ∈ D Generate θdd ∼ Dirichlet(·|αd ) For each tag index n ∈ 1, . . , |T d |: d Generate zdn ∈ 1, . . , K ∼ M ultinomial(·|θdd ) Generate tag tddn ∈ 1, . . , |T | ∼ M ultinomial(·|βzt d ) dn For each expanded document T u,d , u ∈ U d u Generate θdu ∼ Dirichlet(·|αu ) For each tag index n ∈ 1, .

The terms selected for the global model cannot be used for the local model either because a limited number of terms concentrated in a semantic category is used in deep categories. Parameter estimation is similar to that of the global model except that it is done for selected training data corresponding to candidate categories. 4 Dynamic Determination of a Mixture Weight The main step in classification is to properly combine local and global models for each candidate category node by determining a proper mixture weight.

1–16 (2001) 14. : A scalability analysis of classifiers in text categorization. In: 26th ACM SIGIR, pp. 96–103 (2003) 15. : Combining global and local information for enhanced deep classification. In: 2010 ACM SAC, pp. 1760–1767 (2010) 16. : Hierarchically classifying documents using very few words. In: International Conference on Machine Learning, pp. 170–178 (1997) 17. : A comparative study on feature selection in text categorization. In: International Conference on Machine Learning, pp. edu 2 Abstract.

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