Advances in Information Retrieval: 33rd European Conference - download pdf or read online

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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 e-book constitutes the refereed lawsuits 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 offered including 24 poster papers, 17 brief papers, and six instrument demonstrations have been conscientiously 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 conception, multimedia IR, IR purposes, 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

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To alleviate this problem in representing top-level categories, 15,000 terms are selected using the chi-square feature selection method, which is known for the best performing one in text classification [17]. The prior probability is estimated as follows: Di P (ciglobal ) = (5) D where D is the entire document collection and Di is a sub-collection in ciglobal . The conditional probability is estimated by mixture of P(t j | ciglobal ) and P (t j ) to avoid zero probabilities. global P(t j | ci ) = (1 − α ) Pglobal (t j | ci global ) + α Pglobal (t j ) (6) where α is a mixture weight (0 ≤ α ≤ 1) .

Similar as Folk-LDA, β t in β ,β have their distinct topic-specific distributions(β Folk-MM-LDA is estimated from a globally related context through expansion, which demonstrates its advantage over MM-LDA [20] in the general case where few tags are available. Folk-MM-LDA can be seen as an extension of MM-LDA by further considering the expanded tag document information. The variational inference methods used by Folk-MM-LDA and MM-LDA are listed in Table 2. Table 2. Variational methods for Folk-MM-LDA and MM-LDA.

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, . . , |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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