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

ISBN-10: 3642201601

ISBN-13: 9783642201608

ISBN-10: 364220161X

ISBN-13: 9783642201615

This publication constitutes the refereed lawsuits of the thirty third annual ecu convention on info Retrieval examine, 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 rigorously reviewed and chosen from 223 complete learn paper submissions and sixty four poster/demo submissions. The papers are geared up in topical sections on textual content categorization, recommender structures, internet IR, IR evaluate, IR for Social Networks, cross-language IR, IR idea, multimedia IR, IR functions, interactive IR, and query answering /NLP.

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

Example text

Moreover, they found that using tags alone achieves better performance on topic-specific and densely labeled document collections. In their experiments, each document had 1,307 tag tokens on average. Motivated by their observation, we are attempting to answer the following question in this work: can we extend the advantage of using only tags on document clustering to more general and large-scale web pages? It turns out the tag-based web document clustering is challenging because most web pages have few annotations.

He developed one of the most commonly used datasets for computing semantic relatedness of words, as well as formulated a methodology for parameterized generation of labeled datasets for text categorization. Evgeniy taught multiple tutorials on computational advertising and organized a number of workshops at top conferences, including a workshop on feature generation and selection for information retrieval at SIGIR, and workshops on the synergy between user-contributed knowledge and research in AI at IJCAI and AAAI.

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, . . , |T u,d |: u u ∈ 1, . . , K ∼ M ultinomial(·|θdu ) Generate zdun u u ) Generate tag tdun ∈ 1, . . , |T | ∼ M ultinomial(·|βztdun Specifically, we use a fully factorized distribution to approximate the posterior distribution of the latent variables as follows: |U d | |T d | |D| θ d , θ u , zd , zu |γ γ d , γ u , φd , φ u ) = q(θ d |φd q(zdn dn ) q(θdd |γdd ) n=1 d=1 |T u,d | u u q(θdu |γdu ) u q(zdun |φu dun ) n=1 u=1 u where γdd , γdu are Dirichlet parameters and φddn , φudun are multinomial parameters.

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Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings by Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee, Vanessa Mudoch (eds.)


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