Advances in Information Retrieval: 34th European Conference by Vera Hollink, Jiyin He, Arjen de Vries (auth.), Ricardo PDF

By Vera Hollink, Jiyin He, Arjen de Vries (auth.), Ricardo Baeza-Yates, Arjen P. de Vries, Hugo Zaragoza, B. Barla Cambazoglu, Vanessa Murdock, Ronny Lempel, Fabrizio Silvestri (eds.)

ISBN-10: 3642289967

ISBN-13: 9783642289965

ISBN-10: 3642289975

ISBN-13: 9783642289972

This ebook constitutes the complaints of the thirty fourth eu convention on IR learn, ECIR 2012, held in Barcelona, Spain, in April 2012. The 37 complete papers, 28 poster papers and seven demonstrations offered during this quantity have been conscientiously reviewed and chosen from 167 submissions. The contributions are prepared in sections named: question illustration; blogs and online-community seek; semi-structured retrieval; review; purposes; retrieval types; picture and video retrieval; textual content and content material class, categorisation, clustering; platforms potency; music; and posters.

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Additional resources for Advances in Information Retrieval: 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings

Example text

Therefore, it does not require any familiarity with the taxonomy or any other linguistic or semantic tooling. Note that the compression is lossless in terms of recall, since the surviving templates cover exactly the same target queries as the original set. The model’s click-through rate does not deteriorate, and in theory may even improve (the removed templates might have covered instances of non-target queries that were not accounted for by templates of higher rank). Therefore, compression reduces the model’s size with no ill-effects on its quality metrics.

11] propose a bottom-up generalization algorithm utilizing syntactic and semantic constraints to identify useful query patterns which are essentially rules associating generalized keywords with the target articles. Similarly, Pujeri and Karthik [13] propose a tree mining technique to discover templates from Web logs that predict users’ future requests. Wang and Zhai mine query logs to find term association patterns such as context-sensitive term substitutions and additions. Agarwal et al. [1] propose a probabilistic inference framework that is effective on finding structured query patterns that predict the query intents against “classify-then-match” baselines.

37. 2 Analysis Workflow In practice, the target recall parameter’s choice might not be known in advance. A human evaluator (product owner) might need to assess a variety of tradeoff points between recall and click-through rate to decide on the desired setting. , the produced model’s size. Therefore, we are interested in solving the optimization problem for multiple working points, each capturing a different click-through-versus-recall tradeoff. Each data point translates to a different trained model, which is evaluated on a test set – an independent sample of the search log.

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Advances in Information Retrieval: 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings by Vera Hollink, Jiyin He, Arjen de Vries (auth.), Ricardo Baeza-Yates, Arjen P. de Vries, Hugo Zaragoza, B. Barla Cambazoglu, Vanessa Murdock, Ronny Lempel, Fabrizio Silvestri (eds.)


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