Inferring the score distribution of relevant and non-relevant documents is an essential task for many IR applications (e.g. information filtering, recall-oriented IR, meta-search,...
Relying on the Cluster Hypothesis, which states that relevant documents tend to be more similar one to each other than to non-relevant ones, most of information retrieval systems p...
Sylvain Lamprier, Tassadit Amghar, Bernard Levrat,...
The normal practice of selecting relevant documents for training routing queries is to either use all relevants or the 'best n' of them after a (retrieval) ranking opera...
There is a reservoir of knowledge in data from the TREC evaluations that analysis of precision and recall leaves untapped. This knowledge leads to better understanding of query ex...
The Internet is a tremendous resource where one can find documents to enrich a personal information space. The question is: how can one find relevant documents and how can these b...
Information Retrieval Systems aim at retrieving relevant documents according to the information needs which users express. Most Information Retrieval Systems focus on passage retr...
The paper presents two approaches to interactively refining user search formulations and their evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12...
In some information retrieval scenarios, for example internal help desk systems, texts are entered into the document collection without proofreading. This can result in a relative...
In this paper we combine two existing resource selection approaches, CORI and the decision-theoretic framework (DTF). The state-of-the-art system CORI belongs to the large group of...
Traditional information retrieval (IR) systems respond to user queries with ranked lists of relevant documents. The separation of content and structure in XML documents allows indi...