Advances in Information Retrieval: 33rd European Conference by Kalervo Järvelin (auth.), Paul Clough, Colum Foley, Cathal

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

This booklet constitutes the refereed lawsuits of the thirty third annual ecu 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 device demonstrations have been conscientiously 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 review, IR for Social Networks, cross-language IR, IR concept, multimedia IR, IR purposes, interactive IR, and query answering /NLP.

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The ancestor-assistant strategy utilizes the structure of the hierarchy. For each candidate, it borrows the documents of its ancestors all the way up until a common ancestor shared by other candidate node appear in the hierarchy. The neighborassistant strategy, an extension of the ancestor-assistant strategy, borrows documents not only from the ancestors but also from the children of the ancestors. The neighborassistant strategy produces slightly better performance than the ancestor-assistant strategy but requires more time [15].

E. ciglobal = A and cilocal =A/B/C for ci =A/B/C. 3 Global and Local Models The aim of the global model is to utilize information in the top-level categories under the root node. Because of the topical diversity of the categories in a hierarchy, the vocabulary size is very large although the number of training data is usually small. 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].

From the figure, we also observe the benefit of incorporating expanded tag terms in LDA-M on clustering merged multi-topic documents. The figure shows LDA-M outperforms KMeans-M by 54% at most and by 27% on average. Though KMeans-M also improves the clustering quality, the results tend to go down as more users are involved, which shows kmeans is sensitive to the introduction of other topic tags. 3 Usage Comparison of Expanded Tag Documents These experiments are designed to evaluate if independent modeling of the original document and expanded document outperforms the heuristic merging method on clustering quality.

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