Get Advances in Information Retrieval: 33rd European Conference PDF

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 booklet constitutes the refereed complaints of the thirty third annual eu convention on details Retrieval study, 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 software demonstrations have been rigorously reviewed and chosen from 223 complete study paper submissions and sixty four poster/demo submissions. The papers are geared up in topical sections on textual content categorization, recommender platforms, net IR, IR review, IR for Social Networks, cross-language IR, IR concept, multimedia IR, IR functions, interactive IR, and query answering /NLP.

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Read Online or Download Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings PDF

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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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5. 1 0 1 2 3 4 5 6 7 8 9 Level Fig. 6. Comparisons among EDC and RBO with Add-One Smoothing, and RBO with Interpolation Performance with Different Categories. With the limited space and resources required to run the experiments for all the categories in the hierarchy, we show the performance for the top-level categories only. As shown in Table 2, PDC-CBO and PDC-RBO show the remarkable improvements over DC and EDC across different categories. 83% improvements, respectively, compared to EDC. 4428.

This dramatically reduces the factors that lead to topic drift in merged tag document. 4 Comparison of Clustering Based on Tags and/or Words Since user-related tag expansion greatly improves the performance of tag based web document clustering, it is natural to consider the following questions:(1)Are tags a better resource than words on document clustering? (2)How efficient tagbased document clustering is? We further conducted the experiments and compared clustering performance based on different information sources: (1) using only words (KMeans-W, LDAW) (2) using only tags (LDA-M) (3) combining words and tags (MM-LDA, Folk-MM-LDA).

Finally classification is performed after training a classifier for the candidate P. Clough et al. ): ECIR 2011, LNCS 6611, pp. 7–18, 2011. -S. Oh, Y. -H. Myaeng categories using the training documents. By focusing on highly relevant categories from the entire hierarchy, this approach can alleviate error propagation and time complexity problems of a pure machine-leaning based algorithm. Despite the improved performance, the afore mentioned approach suffers from a relatively small number of training documents that are local to a category in a huge hierarchy.

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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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