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Title: The Open University at TREC 2007 enterprise track expert search task.
Authors: Zhu, Jianhan
Song, Dawei
Ruger, Stefan
Editors: Voorhees, E. M.
Buckland, Lori P.
Keywords: Expert search
Issue Date: Feb-2008
Publisher: NIST
Citation: ZHU, J., SONG, D. and RUGER, S., 2008. The Open University at TREC 2007 enterprise track expert search task. In: E. M. VOORHEES and L. P. BUCKLAND, eds. NIST Special Publication 500-274: The Sixteenth Text REtrieval Conference Proceedings (TREC 2007). 5-9 November 2007. Gaithersburg, MD: NIST.
Series/Report no.: NIST Special Publication
SP 500-274
Abstract: The Multimedia and Information Systems group at the Knowledge Media Institute of the Open University participated in the Expert Search and Document Search tasks of the Enterprise Track in TREC 2007. In both the document and expert search tasks, we have studied the effect of anchor texts in addition to document contents, document authority, url length, query expansion, and relevance feedback in improving search effectiveness. In the expert search task, we have continued using a two-stage language model consisting of a document relevance and cooccurrence models. The document relevance model is equivalent to our approach in the document search task. We have used our innovative multiple-window-based cooccurrence approach. The assumption is that there are multiple levels of associations between an expert and his/her expertise. Our experimental results show that the introduction of additional features in addition to document contents has improved the retrieval effectiveness.
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