Download Advances in Information Retrieval: 32nd European Conference by Cathal Gurrin PDF

By Cathal Gurrin

These complaints comprise the papers offered at ECIR 2010, the thirty second european- pean convention on info Retrieval. The convention used to be organizedby the information Media Institute (KMi), the Open college, in co-operation with Dublin urban college and the college of Essex, and was once supported by means of the knowledge Retrieval professional crew of the British computing device Society (BCS- IRSG) and the distinctive curiosity crew on info Retrieval (ACM SIGIR). It was once held in the course of March 28-31, 2010 in Milton Keynes, united kingdom. ECIR 2010 acquired a complete of 202 full-paper submissions from Continental Europe (40%), united kingdom (14%), North and South the US (15%), Asia and Australia (28%), center East and Africa (3%). All submitted papers have been reviewed through at leastthreemembersoftheinternationalProgramCommittee.Outofthe202- pers forty four have been chosen asfull researchpapers. ECIR has alwaysbeen a convention with a powerful pupil concentration. to permit as a lot interplay among delegates as attainable and to maintain within the spirit of the convention we made up our minds to run ECIR 2010 as a single-track occasion. consequently we made up our minds to have presentation codecs for complete papers. a few of them have been provided orally, the others in poster layout. The presentation structure doesn't symbolize any di?erence in caliber. as a substitute, the presentation structure used to be made up our minds after the total papers were accredited on the software Committee assembly held on the college of Essex. The perspectives of the reviewers have been then considered to choose the main applicable presentation layout for every paper.

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Additional resources for Advances in Information Retrieval: 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010.Proceedings

Example text

Table 2. 57 By Topic. For the best-performing configuration of each method (as given in Table 1), we compute retrieval-effectiveness measures at cut-off level k = 10 and group them by topic. Table 2 shows the resulting figures. These support our above observations. In addition, we observe that all methods perform worst on queries from Technology. The best performance varies per method and measure. Table 3. 35 By Temporal Granularity. In analogy, we can group retrieval-effectiveness measurements at cut-off level k = 10 by temporal granularity – again considering only the best-performing configuration of each method.

1 Vector Space Model The vector space model[13] is one of the approaches that we have applied to represent the documents. Methods based on term cooccurrence[15] have been used very frequently to identify semantic relationships among documents. 2 2 D 1 D2 |D 1 ||D2 | (2) (3) (4) Language Model Approach We have also applied a language model approach to rank the set of candidate documents. In this case we look at the differences in the term distribution between two documents by computing the Kullback-Leibler divergence: KLD(D1 ||D2 ) = PD1 (t)log t∈D1 PD1 (t) PD2 (t) (5) where PD1 (t) is the probability of the term t in the reference document, and PD2 (t) is the probability of the term t in the candidate document.

The probability of generating the time interval [qb , qe ] from a temporal expression T is defined as P ( [qb , qe ] | T ) = 1 ½( [qb , qe] ∈ T ) |T | (7) where ½( [qb , qe ] ∈ T ) is an indicator function whose value is 1 iff [qb , qe ] ∈ T . For T we thus also assume all time intervals that it can refer to as equally likely. Putting (6) and (7) together we obtain P(Q | T ) = 1 |Q| 1 ½( [qb , qe] ∈ T ) , |T | (8) |T ∩ Q| . , it is not clear which time interval the user issuing the query and author writing the document had in mind when uttering Q and T , respectively.

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