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Zhu, Jianhan; Song, Dawei and Rüger, Stefan
(2007).
URL: http://trec.nist.gov/pubs/trec16/papers/openu.ent....
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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- Item ORO ID
- 36076
- Item Type
- Conference or Workshop Item
- Extra Information
-
NIST Special Publication 500-274:
The Sixteenth Text REtrieval Conference Proceedings (TREC 2007) - Academic Unit or School
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Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications - Copyright Holders
- © 2007 Not known
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- Depositing User
- Danielle Lilly