By W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)
This publication constitutes the refereed lawsuits of the thirtieth annual eu convention on details Retrieval learn, ECIR 2009, held in Toulouse, France in April 2009.
The forty two revised complete papers and 18 revised brief papers awarded including the abstracts of three invited lectures and 25 poster papers have been rigorously reviewed and chosen from 188 submissions. The papers are geared up in topical sections on retrieval version, collaborative IR / filtering, studying, multimedia - metadata, specialist seek - advertisements, evaluate, opinion detection, internet IR, illustration, clustering / categorization in addition to dispensed IR.
Read or Download Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings PDF
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Additional resources for Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
Furthermore, by representing a document as mixture of topics, complicated topics models may help to discover some ﬁne-grained topics related to a query in the relevant documents. To investigate these issues, we describe several ways in which topic models have been incorporated into retrieval. We cannot possibly study all topic modeling approaches, so we select a few that are representative: the well-known Mixture of Unigrams (MU) model ; Latent Dirichlet Allocation (LDA) , a more complicated and computationally expensive topic model; and Pachinko Allocation Model (PAM) , a recently proposed new topic model which not only models the relations between words and identiﬁes topics but also models the organization and co-occurrences of the topics themselves.
Otherwise, n-call is zero. Averaging over multiple queries yields the mean n-call. The two extremes are 10-call, an ambitious metric of perfect precision, returning only relevant documents, and 1-call is a conservative metric that is satisﬁed with only one relevant document. Therefore, a risk-aversion approach, which can reliably ﬁnd one relevant document with small variance, is preferred for 1-call, while a risk-loving approach, which gives small weight to the variance, is favored for 10-call.
Fig. 2 (a) plots the expected overall relevance and variance against the diﬀerent values of the parameter α, where the left y axis corresponds to the expected relevance of top-10 ranked documents and the right y axis shows the variance (uncertainty) of the list. The graph demonstrates that when we set a small α, the optimal rank list will have a relatively large variance (risk). As a reward of taking such risk, the expected relevance of the list stays high. But as the parameter α increases, the ranking becomes more risk-aversion.
Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings by W. Bruce Croft (auth.), Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy (eds.)