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

By Cathal Gurrin

These complaints include the papers offered at ECIR 2010, the thirty second ecu- pean convention on info Retrieval. The convention used to be organizedby the data Media Institute (KMi), the Open collage, in co-operation with Dublin urban college and the collage of Essex, and used to be supported via the data Retrieval expert workforce of the British machine Society (BCS- IRSG) and the targeted curiosity crew on details 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%), heart East and Africa (3%). All submitted papers have been reviewed by means of at leastthreemembersoftheinternationalProgramCommittee.Outofthe202- pers forty four have been chosen asfull researchpapers. ECIR has alwaysbeen a convention with a robust pupil concentration. to permit as a lot interplay among delegates as attainable and to maintain within the spirit of the convention we determined to run ECIR 2010 as a single-track occasion. for that reason 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 characterize any di?erence in caliber. in its place, the presentation layout used to be made up our minds after the total papers have been 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 structure for every paper.

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

Example text

All data was kept in an Oracle 11g database. Temporal expressions were extracted using TARSQI [12]. TARSQI detects and resolves temporal expressions using a combination of hand-crafted rules and machine learning. It annotates a given input document using the TimeML [13] markup language. Building on TARSQI’s output, we extracted range temporal expressions such as “from 1999 until 2002”, which TARSQI does not yet support. Further, we added each article’s publication date as an additional temporal expression.

Building on TARSQI’s output, we extracted range temporal expressions such as “from 1999 until 2002”, which TARSQI does not yet support. Further, we added each article’s publication date as an additional temporal expression. We map temporal expressions to our quadruple representation using milliseconds as chronons and the UNIX epoch as our reference time-point. 2 Experimental Results We measure the retrieval effectiveness of the methods under comparison using Precision at k (P@k) and nDCG at k (N@k) as two standard measures.

2 K. Berberich et al. Uncertainty-Aware Language Model As explained in the introduction, for many temporal expressions the exact time interval that they refer to is uncertain. Our second approach LmtU explicitly considers this uncertainty. In detail, we define the probability of generating Q from the document d as P(Q | T ) = 1 |Q| P ( [qb , qe ] | T ) , (6) [qb , qe ]∈Q where the sum ranges over all time intervals included in Q. The approach thus assumes equal likelihood for each time interval [qb , qe ] that Q can refer to.

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