New PDF release: Advances in Information Retrieval: 32nd European Conference

By Cathal Gurrin

ISBN-10: 3642122752

ISBN-13: 9783642122750

These complaints comprise the papers offered at ECIR 2010, the thirty second european- pean convention on info Retrieval. The convention was once organizedby the information Media Institute (KMi), the Open college, in co-operation with Dublin urban collage and the college of Essex, and was once supported via the data Retrieval professional team of the British computing device Society (BCS- IRSG) and the certain curiosity workforce 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 via 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 made up our minds 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 offered orally, the others in poster structure. The presentation layout doesn't symbolize any di?erence in caliber. as a substitute, the presentation structure used to be determined after the entire papers were approved on the software Committee assembly held on the college of Essex. The perspectives of the reviewers have been then considered to choose the main acceptable presentation layout for every paper.

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

Example text

I don’t know ) for 4% of the total 6, 255 relevance assessments. Relevance assessments with the last option are ignored when computing retrieval-effectiveness measures below. Implementation Details. We implemented all methods in Java. 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.

Both share the same formal representation in our model, so that LmT can generate a query containing one of them from a document containing the other. , one not paying special attention to temporal expressions), would not be aware of the semantic connection between the textual terms ’80s and 1980s. 2 Proofs and additional details are provided in our accompanying technical report [10]. 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.

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. 3 Performance of Ranking Methods We have applied the approaches described above to different elements from the parent, cache and candidate pages. Specifically, we have studied the similarity among the following pairs of elements: (i) parent anchor text & candidate title, (ii) parent anchor text & candidate content, (iii) parent content & candidate content, and (iv) cache content & candidate content.

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

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