This site uses cookies to deliver our services and to ensure you get the best experience. By continuing to use this site, you consent to our use of cookies and acknowledge that you have read and understand our Privacy Policy, Cookie Policy, and Terms
Abstract. A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible interpretations of a que...
Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies ...
Eduard Hoenkamp, Peter Bruza, Dawei Song, Qiang Hu...
Abstract. In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain; strategies are thus needed for maximizing t...
This paper presents a theoretical methodology to evaluate filters in XML retrieval. Theoretical evaluation is concerned with the formal investigation of qualitative properties of r...
Abstract. A mismatch between differenteventspaceshasbeen used toargue against rank equivalence of classic probabilistic models of information retrieval and language models. We ques...
The paper makes three points of significance for IR research: (1) The Cranfield paradigm of IR evaluation seems to lose power when one looks at human instead of system performance....
Abstract. In the rapidly evolving and growing environment of the internet, web site owners aim to maximize interest for their web site. In this article we propose a model, which co...
In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques i...
Haiming Liu 0002, Victoria S. Uren, Dawei Song, St...
We introduce perturbation kernels, a new class of similarity measure for information retrieval that casts word similarity in terms of multi-task learning. Perturbation kernels mode...