Abstract
An important challenge for biomedical information retrieval (IR) is dealing with the complex, inconsistent and ambiguous biomedical terminology. Frequently, a concept-based representation defined in terms of a domain-specific terminological resource is employed to deal with this challenge. In this paper, we approach the incorporation of a concept-based representation in monolingual biomedical IR from a cross-lingual perspective. In the proposed framework, this is reali
sed by translating and matching between text and concept-based representations. The approach allows for deployment of a rich set of techniques proposed and evaluated in traditional cross-lingual IR. We compare six translation models and measure their effectiveness in the biomedical domain. We demonstrate that the approach can result in significant improvements in retrieval effectiveness over word-based retrieval. Moreover, we demonstrate increased effectiveness of a CLIR framework for monolingual biomedical IR if basic translations models are combined.
Original language | Undefined |
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Title of host publication | Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM '10) |
Editors | J. Huang, N. Koudas, G. Jones, Xindong Wu, K. Collins-Thompson, Aijun An |
Place of Publication | New York |
Publisher | Association for Computing Machinery |
Pages | 169-178 |
Number of pages | 10 |
ISBN (Print) | 978-1-4503-0099-5 |
DOIs | |
Publication status | Published - 2010 |
Event | 19th ACM International Conference on Information and Knowledge Management, CIKM 2010 - Toronto, Canada Duration: 26 Oct 2010 → 30 Oct 2010 Conference number: 19 |
Publication series
Name | |
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Publisher | ACM |
Conference
Conference | 19th ACM International Conference on Information and Knowledge Management, CIKM 2010 |
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Abbreviated title | CIKM |
Country/Territory | Canada |
City | Toronto |
Period | 26/10/10 → 30/10/10 |
Keywords
- IR-72470
- METIS-270940
- Biomedical information retrieval
- CR-H.3.3
- Language modeling
- TREC Genomics
- EWI-18213
- CLIR framework