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Re-ranking with context for high-performance biomedical information retrieval
- Source :
- International journal of data mining and bioinformatics. 6(2)
- Publication Year :
- 2012
-
Abstract
- In this paper, we present a context-sensitive approach to re-ranking retrieved documents for further improving the effectiveness of high-performance biomedical literature retrieval systems. For each topic, a two-dimensional positive context is learnt from the top N retrieved documents and a group of negative contexts are learnt from the last N′ documents in initial retrieval ranked list. The contextual space contains lexical context and conceptual context. The probabilities that retrieved documents are generated within the contextual space are then computed for document re-ranking. Empirical evaluation on the TREC Genomics full-text collection and three high-performance biomedical literature retrieval runs demonstrates that the context-sensitive re-ranking approach yields better retrieval performance.
- Subjects :
- Cognitive models of information retrieval
Information retrieval
Technology Assessment, Biomedical
Computer science
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
Information Storage and Retrieval
TREC Genomics
Context (language use)
Genomics
Library and Information Sciences
Space (commercial competition)
General Biochemistry, Genetics and Molecular Biology
Human–computer information retrieval
Re ranking
Vector space model
Relevance (information retrieval)
Algorithms
Information Systems
Subjects
Details
- ISSN :
- 17485673
- Volume :
- 6
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- International journal of data mining and bioinformatics
- Accession number :
- edsair.doi.dedup.....09d21032ab3aa1a068f54cc595ed02c0