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Document re-ranking by generality in bio-medical information retrieval

Yan, Xin; Li, Xue and Song, Dawei (2005). Document re-ranking by generality in bio-medical information retrieval. In: ed. Web Information Systems Engineering – WISE 2005. Lecture Notes in Computer Science, Volume 380 (2806/2005). Berlin: Springer, pp. 376–389.

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Document ranking is an important process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. Traditional document ranking methods are mostly based on the similarity computations between documents and query. In this paper we argue that the similarity-based document ranking is insufficient in some cases. There are two reasons. Firstly it is about the increased information variety. There are far too many different types documents available now for user to search. The second is about the users variety. In many cases user may want to retrieve documents that are not only similar but also general or broad regarding a certain topic. This is particularly the case in some domains such as bio-medical IR. In this paper we propose a novel approach to re-rank the retrieved documents by incorporating the similarity with their generality. By an ontology-based analysis on the semantic cohesion of text, document generality can be quantified. The retrieved documents are then re-ranked by their combined scores of similarity and the closeness of documents’ generality to the query’s. Our experiments have shown an encouraging performance on a large bio-medical document collection, OHSUMED, containing 348,566 medical journal references and 101 test queries.

Item Type: Book Section
ISBN: 3-540-30017-1, 978-3-540-30017-5
Keywords: Generality; relevance; document ranking
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 9323
Depositing User: Users 6898 not found.
Date Deposited: 28 Sep 2007
Last Modified: 02 May 2018 12:48
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