By Mounia Lalmas, Andrew MacFarlane, Stefan Rüger, Anastasios Tombros, Theodora Tsikrika, Alexei Yavlinsky

ISBN-10: 3540333479

ISBN-13: 9783540333470

th those lawsuits include the refereed papers and posters awarded on the 28 Annual ecu convention on info Retrieval (ECIR 2006), which used to be held at Imperial university London in South Kensington among April 10 and 12, 2006. ECIR is the once a year convention of the British desktop Society’s Inf- mation Retrieval expert crew. the development begun its existence as a colloquium in 1978 and used to be held within the united kingdom every year until eventually 1998, whilst the development came about in Grenoble, France. due to the fact that then the venue has alternated among the united kingdom and Continental Europe. within the final decade ECIR has grown to turn into the key Europeanforumforthediscussionofresearchinthe?eldofinformationretrieval. ECIR 2006 bought 177 paper and seventy three poster submissions, mostly from the united kingdom (18%) and Continental Europe (50%), yet we had many sub- missions from furthera?eldincludingAmerica(7%),Asia(21%),Middle EastandAfrica(2%), and Australasia (2%). In overall 37 papers and 28 posters have been authorized, and papers have been switched over to posters. All contributions have been reviewed through at the least 3 reviewers in a double nameless strategy after which ranked in the course of a ProgrammeCommittee assembly with respectto scienti?c caliber andoriginality. it's a solid and fit signal for info retrieval commonly, and ECIR particularly, that the submission cost has greater than doubled during the last 3 years. the disadvantage, in fact, is that many top of the range submissions needed to be rejected because of a restricted capability of the conference.

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20. R AGHAVAN , V. , AND W ONG , S. K. A critical analysis of the vector space model for information retrieval. Journal of the American Society for Information Science 37, 5 (1986), 279–287. 21. R ENYI , A. Foundations of probability. Holden-Day Press, San Francisco, USA, 1969. 22. , AND WALKER , S. Some simple approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval. In Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval (Dublin, Ireland, June 1994), Springer-Verlag, pp.

At 5 Prec. 4430 1. DFR approach computes the probability of observing two probabilities, while LM smoothes the MLE of a term in the document. 2. DFR approach weights terms according to the improbability of observing the MLE of a term in the document given the prior, and it is based on information theoretic notions, such as amount of information and uncertainty. LM instead weights the probability of observing the term in a document given a prior distribution. 3. In DFR approach there are no non-zero probabilities, that is when a term does not occur in a document it does not contribute at all to the document score.

This requires extra computational costs either in terms of additional index or retrieval structures. 4. The basic DFR models (such as Formulas 10, 8 and 9) can be used as they are for query expansion. A parameter free model of query expansion can be also defined [3]. Also Kullback-Leibler divergence based techniques for query expansion [8, 26], as it was here shown, are approximations of the hypergeometric model and the binomial model. 5. With DFR approach we can combine LM with DFR models or BM25 into a single model, with the advantage of not having non-zero probabilities [1, 13].

### Advances in Information Retrieval: 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006. Proceedings by Mounia Lalmas, Andrew MacFarlane, Stefan Rüger, Anastasios Tombros, Theodora Tsikrika, Alexei Yavlinsky

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