CLEF 2010 Call for lab proposals
The CLEF 2010 conference is the continuation of the popular CLEF campaigns and workshops that have run for the past ten years (2000-2009). It will cover a broad range of issues from fields of multilingual and multimodal information access evaluation. One of the two main parts of CLEF 2010 will be a series of "labs". Two different forms of labs will be offered: labs can either be run "campaign-style" during the twelve month period preceding the conference, or adopt a more "workshop"-style format that can explore issues of information access evaluation and related fields. The labs will culminate in sessions of a half-day, one full day or two days at the CLEF 2010 conference. Read more ...

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IPM paper online
Conceptual Languages for Domain-Specific Retrieval by Edgar Meij, Dolf Trieschnigg, Maarten de Rijke and Wessel Kraaij was accepted for publication in Information Processing and Management a while back; it is available now. Over the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retrieval. By means of relevance feedback the original query is translated into a conceptual representation, which is subsequently used to update the query model.

Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms.

Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.

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