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 eu convention on details Retrieval study, ECIR 2011, held in Dublin, eire, in April 2010. The forty five revised complete papers awarded including 24 poster papers, 17 brief papers, and six device demonstrations have been rigorously reviewed and chosen from 223 complete examine 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 review, IR for Social Networks, cross-language IR, IR thought, multimedia IR, IR purposes, interactive IR, and query answering /NLP.

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Additional 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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Tags of original documents are all used. The tag terms are tokenized and stemmed by Lemur toolkit5 . The maximum number of URL tagging users ranges from 1 to 5. These parameter settings are also applied in later experiments. 05 0 1 2 3 4 # of Users 5 1 2 3 # of Users 4 5 Fig. 4. Comparison of F1 scores on clustering the documents under the “Top” node Figure 4 gives the comparison of clustering results under the “Top” node. Significant improvements are found by comparing methods with expansion (KMeansM, LDA-M) to corresponding methods without expansion (KMeans-T, LDA-T).

359–367 (1998) 10. : Hierarchical document categorization with support vector machines. In: 13th ACM CIKM, pp. 78–87 (2004) 11. : Yahoo! as an ontology: using Yahoo! categories to describe documents. In: 8th ACM CIKM, pp. 180–187 (1999) 12. : Rule-based text categorization using hierarchical categories. In: IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2827–2830 (1998) 13. : Hierarchical classification of real life documents. In: 1st (SIAM) International Conference on Data Mining, pp.

In the first stage, a virtual document T u,d for each user u of each document d is constructed. T u,d is made up of the tags in u’s bookmarks which match the tag(s) of document d labeled by user u. Note that T u,d may incorporate hypernyms, synonyms or other descriptive tags of related topics from the user’s perspective. In this process, we consider the tag expansion in the scope of users who have labeled the original document. Alternative expansion methods, such as expansion based on tag co-occurrence only without user constraints, have also been examined.

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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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