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.The work reported in this paper is funded in part by an IBM UIMA innovation award and the JISC (Joint Information Systems Committee) funded DYNIQX (Metadata-based DYNamIc Query Interface for Cross(X)-searching content resources) project. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA)
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