Advances in Information Retrieval: 34th European Conference by Vera Hollink, Jiyin He, Arjen de Vries (auth.), Ricardo

By Vera Hollink, Jiyin He, Arjen de Vries (auth.), Ricardo Baeza-Yates, Arjen P. de Vries, Hugo Zaragoza, B. Barla Cambazoglu, Vanessa Murdock, Ronny Lempel, Fabrizio Silvestri (eds.)

This booklet constitutes the lawsuits of the thirty fourth eu convention on IR learn, ECIR 2012, held in Barcelona, Spain, in April 2012. The 37 complete papers, 28 poster papers and seven demonstrations provided during this quantity have been rigorously reviewed and chosen from 167 submissions. The contributions are geared up in sections named: question illustration; blogs and online-community seek; semi-structured retrieval; overview; purposes; retrieval versions; photograph and video retrieval; textual content and content material class, categorisation, clustering; platforms potency; tune; and posters.

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Extra resources for Advances in Information Retrieval: 34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings

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Hyponymy Extraction and Web Search Behavior Analysis Based on Query Reformulation. , David, N. ) IBERAMIA 2008. LNCS (LNAI), vol. 5290, pp. 332–341. : Quantifying query ambiguity. In: HLT 2002, pp. : Overview of iCLEF 2009: exploring search behaviour in a multilingual folksonomy environment. In: CLEF 2009, pp. : Combining evidence for automatic web session identification. : Using Coherence-Based Measures to Predict Query Difficulty. W. ) ECIR 2008. LNCS, vol. 4956, pp. 689–694. : Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term.

The number of messages clicked per query is much larger. 17 message clicks compared to the 1 or less page clicks associated with SE queries [16]. Secondly, email collections are of course much smaller than the web, which naturally increases the chance of serendipitous overlap. A clearer picture of what is happening in these situations can be attained by investigating the amount of overlap using overlap coefficient as a metric. Given two sets of comparable items S1 and S2 the overlap coefficient is calculated as follows: |S1 ∩ S2 | Overlap(S1 , S2 ) = min(|S1 |, |S2 |) The metric describes how much of the smaller set is included in the larger and is not sensitive to the relative sizes of the two sets.

In: Proc. 14th International World Wide Web Conference (WWW 2005), pp. 391–400 (2005) 10. : Getting work done on the web: supporting transactional queries. In: Proc. 29th Annual International ACM SIGIR Conference (SIGIR 2006), pp. 557–564 (2006) 11. : Mining generalized query patterns from web logs. In: HICSS (2001) 12. : Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995) 13. : Constraint based frequent pattern mining for generalized query templates from web log.

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