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The Open University at TREC 2007 Enterprise Track.

Zhu, Jianhan; Song, Dawei; R�ger, Stefan


Jianhan Zhu

Dawei Song

Stefan R�ger


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.


ZHU, J., SONG, D. and RUGER, S. 2007. The Open University at TREC 2007 Enterprise Track. In Proceedings of the 16th Text retrieval conference (TREC 2007), 5-9 November 2007, Gaithersburg, USA. National Institute of Standards and Technology (NIST) special publications, SP 500-274. Gaithersburg: NIST [online], paper number 49. Available from:

Conference Name 16th Text retrieval conference (TREC 2007)
Conference Location Gaithersburg, USA
Start Date Nov 5, 2007
End Date Nov 9, 2007
Acceptance Date Feb 28, 2008
Online Publication Date Feb 28, 2008
Deposit Date May 26, 2009
Publicly Available Date May 26, 2009
Publisher NIST National Institute of Standards and Technology
Article Number 49
Series Title National Institute of Standards and Technology (NIST) special publications
Series Number SP 500-274
Keywords Expert search
Public URL
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