By Andrei Broder (auth.), Giambattista Amati, Claudio Carpineto, Giovanni Romano (eds.)
This booklet constitutes the refereed court cases of the twenty ninth annual eu convention on info Retrieval study, ECIR 2007, held in Rome, Italy in April 2007. The forty two revised complete papers and 19 revised brief papers offered including three keynote talks and 21 poster papers have been rigorously reviewed and chosen from 220 article submissions and seventy two poster paper submissions. The papers are prepared in topical sections on concept and layout, potency, peer-to-peer networks, end result merging, queries, relevance suggestions, review, type and clustering, filtering, subject id, professional discovering, XML IR, net IR, and multimedia IR.
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Extra info for Advances in Information Retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007. Proceedings
Model-based feedback in the language modeling approach to information retrieval. In Proceedings of CIKM ‘01, pages 403-410, 2001. Multinomial Randomness Models for Retrieval with Document Fields Vassilis Plachouras1 and Iadh Ounis2 1 Yahoo! uk 2 Abstract. Document fields, such as the title or the headings of a document, offer a way to consider the structure of documents for retrieval. Most of the proposed approaches in the literature employ either a linear combination of scores assigned to different fields, or a linear combination of frequencies in the term frequency normalisation component.
Various stemmers exist, including rule-based stemmers  and statistical stemmers . Although stemming can significantly improve matching coverage, it also introduces noise, which can lead to poor matches. Using the Porter stemmer, both “marine vegetation” and “marinated vegetables” stem to “marin veget”, which is undesirable. Overall, however, the number of meaningful matches introduced typically outweighs the number of spurious matches. Throughout the remainder of this paper, we use the Porter stemmer to generate all of our stemmed representations.
Different estimates can lead to radically different rankings. We now describe how we estimate these models using the representations available to us. We begin with the query model. The most straightforward way of estimating a query model is to use the surface representation. This is estimated as: P( w | θ Q ) = tf w,QS | QS | (2) Similarity Measures for Short Segments of Text 21 where QS denotes the query surface representation, tfw,QS is the number of times w occurs in the representation, and |QS| is the total number of terms in QS.
Advances in Information Retrieval: 29th European Conference on IR Research, ECIR 2007, Rome, Italy, April 2-5, 2007. Proceedings by Andrei Broder (auth.), Giambattista Amati, Claudio Carpineto, Giovanni Romano (eds.)