According to standard pragmatics, we should account for conversational implicatures in terms of Grice’s (1967) maxims of conversation. Neo-Griceans like Atlas & Levinson (1981) and Horn (1984) seek to reduce those maxims to the so-called Q and I-principles. In this paper I want to argue that (i) there are major problems for reducing Gricean pragmatics to these two principles, and (ii) in fact, ...

In this paper we examine the role of explanations as a means of facilitating the use of relevance feedback in information retrieval systems. We do this with particular reference to previous experimental work. This demonstrates that explanations can increase the user’s willingness to interact more fully with the system. We outline the general conclusions from this experimental work and discuss t...

In this paper we propose a novel method for multimedia semantic indexing using model vectors. Model vectors provide a semantic signature for multimedia documents by capturing the detection of concepts broadly across a lexicon using a set of independent binary classifiers. While recent techniques have been developed for detecting simple generic concepts such as indoors, outdoors, nature, manmade...

metasearch engine developed to explore the query of large, distributed, online visual information systems. The current implementation integrates user feedback into a performance-ranking mechanism.

Our goal in this study was to explore the potentials of extracting features from eye-tracking data that have the potential to improve performance in implicit relevance feedback. We view this type of data as an example of the searcher’ immediate context and as containing useful clues of the indications of the interaction between the searcher and the IR system. In particular, we explored if we co...

Background: The PubMed ‘Early Alerts’ provide FDA regulatory reviewers with weekly topical searches of the most recently submitted citations to PubMed/MEDLINE to support prospective detection of emerging adverse drug events for specific drugs. We seek to increase the precision of electronic searching based on an assessment of relevance feedback for a subset of retrieved citations for the antidi...

Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate the effectiveness of the various methods. Prescriptions are given for conducting text retrieval o...

The Interactive Track investigation at Rutgers concentrated primarily on three factors: the searchers’ uses and understandings of relevance feedback and ranked output, and the utility of relevance feedback for the interactive track task; the searchers’ understandings of the interactive track task; and performance differences based on topic characteristics and searcher and order effects. Our off...

The explosive growth of information on the World Wide Web demands eeective intelligent search and ltering methods. Consequently, techniques have been developed that extract conceptual information from documents to build domain models automatically. The model we build is a taxonomy of conceptual terms that is used in a search assistant to help the user navigate to the right set of required docum...

We propose a general relevance model, called the User Relevance Model, that formalises the decisions taken by a user during a query with respect to relevance judgements. Starting from a keyword-based query, the user is allowed to refine the document search using relevance feedback iterations where some subset of the result set is marked as relevant, and another subset is marked as non-relevant....