New PDF release: Advances in Information Retrieval: 29th European Conference

By Andrei Broder (auth.), Giambattista Amati, Claudio Carpineto, Giovanni Romano (eds.)

ISBN-10: 3540714944

ISBN-13: 9783540714941

ISBN-10: 3540714960

ISBN-13: 9783540714965

This ebook constitutes the refereed complaints of the twenty ninth annual ecu convention on info Retrieval study, ECIR 2007, held in Rome, Italy in April 2007. The forty two revised complete papers and 19 revised brief papers offered including three keynote talks and 21 poster papers have been conscientiously reviewed and chosen from 220 article submissions and seventy two poster paper submissions. The papers are prepared in topical sections on concept and layout, potency, peer-to-peer networks, consequence merging, queries, relevance suggestions, assessment, category and clustering, filtering, subject id, specialist discovering, XML IR, net IR, and multimedia IR.

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Additional resources for Advances in Information Retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007. Proceedings

Example text

It uses the unexpanded query representation, the expanded candidate representation, and ranks using the negative KL-divergence, whereas the DenseProb method uses expanded representations for both the query and the candidate and also ranks using the negative KL-divergence. Finally, the Backoff method is a hybrid method that ranks exact matches, exact stems matches, and then DenseProb matches. The goal here is to see what benefit, if any, is achieved by replacing the phrase and subset matches from the Stemming method with DenseProb matches.

Similarity measures for tracking information flow. In Proceedings of CIKM ‘05, pages 517-524, 2005. [8] Murdock, V. B. A Translation Model for Sentence Retrieval. In Proceedings of HLT/EMNLP ‘05, pages 684-691, 2005. [9] Porter, M. F. An algorithm for suffix stripping. Program, 14(3), pages 130-137, 1980. [10] Rocchio, J. J. Relevance Feedback in Information Retrieval, pages 313-323. PrenticeHall, 1971. [11] Sahami, M. and Heilman, T. A web-based kernel function for measuring the similarity of short text snippets.

Such a representation is very sparse. However, it is very high quality because no automatic or manual transformations (such as stemming) have been done to alter it. While it is possible that such transformations enhance the representation, it is also possible that they introduce noise. 2 Stemmed Representation Stemming is one of the most obvious ways to generalize (normalize) text. For this reason, stemming is commonly used in information retrieval systems as a rudimentary device to overcome the vocabulary mismatch problem.

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Advances in Information Retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007. Proceedings by Andrei Broder (auth.), Giambattista Amati, Claudio Carpineto, Giovanni Romano (eds.)

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