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

th those complaints comprise the refereed papers and posters provided on the 28 Annual ecu convention on details Retrieval (ECIR 2006), which was once held at Imperial collage London in South Kensington among April 10 and 12, 2006. ECIR is the once a year convention of the British laptop Society’s Inf- mation Retrieval expert workforce. the development began its existence as a colloquium in 1978 and was once held within the united kingdom every year till 1998, while the development came about in Grenoble, France. given that then the venue has alternated among the united kingdom and Continental Europe. within the final decade ECIR has grown to develop into the key Europeanforumforthediscussionofresearchinthe?eldofinformationretrieval. ECIR 2006 obtained 177 paper and seventy three poster submissions, principally 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 accredited, and papers have been switched over to posters. All contributions have been reviewed by way of at the least 3 reviewers in a double nameless technique after which ranked in the course of a ProgrammeCommittee assembly with respectto scienti?c caliber andoriginality. it's a sturdy and fit signal for info retrieval quite often, and ECIR specifically, that the submission price has greater than doubled over the last 3 years. the disadvantage, in fact, is that many high quality submissions needed to be rejected due to a constrained ability of the conference.

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**Extra info for Advances in Information Retrieval: 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006. Proceedings**

**Sample text**

J. The Estimation of Probabilities: an Essay on Modern Bayesian Methods, vol. 30. T. Press, Cambridge, Massachusetts, 1968. 11. H ARTER , S. P. A probabilistic approach to automatic keyword indexing. PhD thesis, Graduate Library, The University of Chicago, Thesis No. T25146, 1974. 24 G. Amati 12. , AND O UNIS , I. A study of parameter tuning for term frequency normalization. In Proceedings of the twelfth International Conference on Information and Knowledge Management (2005), Springer. 13. , AND O UNIS , I.

Amati P(tf|d, p) = l(d) tf ptf · (1-p)l(d)−tf (1) The best value for the parameter p in the binomial is unknown. We note that the dP(tf|d, p) likelihood P(tf|d, p) is maximised when = 0 which is equivalent to set p to dp the maximum likelihood estimate MLE of the term in the document: ˆ p= tf (MLE) l(d) (2) When the prior p is unknown, then the MLE is a good estimate for p. However we know that the prior probability of occurrence of the term t is the relative term-frequency in the collection: TF (3) P(t) = TFC But, what does happen if we substitute the prior P(t) for p in Equation 1?

Let G={A, B, D} as a weighted graph constructed by A, B and D. = A U B is the vertex set while D = {d ij } is the edge set. WA, WB are the B B B B total weights of A, B respectively. In the weighted graph G, the set of all feasible flows ξ = [fij] from A to B is defined by the following constraints: B B f ij ≥ 0 1 ≤ i ≤ m 1 ≤ j ≤ n n ∑f j =1 m ∑f i =1 ij 1≤ i ≤ m = wai ij = wbj WA WB m n ∑∑ i =1 j =1 (8) (9) 1≤ j ≤ n (10) f ij = WA (11) Constraint (8) allows moving words from A to B and not vice versa.