... we address are threefold: (i) what are the effects of estimating and applying relevance models to the domain specific collection used at CLEF 2008, (ii) what are the results of parsimonizing these relevance models, and (iii) what are the results of applying concept models for blind relevance feedback? Parsimonization is a technique by which the term probabilities in a language model may be re-estimated based on a comparison with a reference model, making the resulting model more sparse and to the point. Concept models are term distributions over vocabulary terms, based on the language associated with concepts in a thesaurus or ontology and are estimated using the documents which are annotated with concepts. Concept models may be used for blind relevance feedback, by first translating a query to concepts and then back to query terms. We find that applying relevance models helps significantly for the current test collection, in terms of both mean average precision and early precision. Moreover, parsimonizing the relevance models helps mean average precision on title-only queries and early precision on title+narrative queries. Our concept models are able to significantly outperform a baseline query-likelihood run, both in terms of mean average precision and early precision on both title-only and title+narrative queries.

Abstract not found

In many collections, documents are annotated using concepts from a structured knowledge source such as an ontology or thesaurus. Examples include the news domain [7], where each news item is categorized according to the nature of the event that took

Historical and heritage collections consist for a considerable part of text and may incorporate diverse text types such as journals, archival documents, and catalogue descriptions. Because of the historical distance, access to this content is not straightforward. Historical variants of text are often more complex to identify and retrieve than modern variants. This is due to the less