By Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee, Vanessa Mudoch (eds.)
This ebook constitutes the refereed complaints of the thirty third annual eu convention on details Retrieval examine, 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 examine paper submissions and sixty four poster/demo submissions. The papers are geared up in topical sections on textual content categorization, recommender platforms, internet IR, IR evaluate, IR for Social Networks, cross-language IR, IR concept, 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
Second, if this integration succeeds, how valuable is the information that user feedback provides? For the tasks of retrieval evaluation and query disambiguation, the talk will quantify by how much user feedback can save human annotator eﬀort and improve retrieval quality respectively. Bio. Thorsten Joachims is an Associate Professor in the Department of Computer Science at Cornell University. In 2001, he ﬁnished his dissertation with the title “The Maximum-Margin Approach to Learning Text Classiﬁers: Methods, Theory, and Algorithms”, advised by Prof.
Directory as it goes to deeper levels. This is caused by propagations of errors at early stages involving the ancestors in classifications. In , two refinement methods using cross-validation and metafeatures which are the predication of children of a target node are introduced to deal with error propagation and nonlinearity of general concepts in high level. Text Classification for a Large-Scale Taxonomy 9 In , shrinkage approach was proposed for hierarchical classification. Probabilities of words for a class corresponding to a leaf node are calculated for the parents up to the root and combined with a set of parameters.
Li et al. Table 3. Results for clustering the documents under the nodes “Top/Computers/Programming/Languages”, “Top/Arts” and “Top”. indicates Folk-LDA achieves a signiﬁcant improvement over the LDA-M method. no. 248 subcategories of “Top” node have little in common and people seldom tag them with common tag terms. 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?
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.)