Re-ranking with context for high-performance biomedical information retrieval

Authors:
Xiaoshi Yin;Jimmy Xiangji Huang;Zhoujun Li
Affiliations:
State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China/ School of Computer Science and Engineering, Beihang University, Beijing 100191, China;School of Information Technology, York University, Toronto M3J 1P3, Canada;State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China/ School of Computer Science and Engineering, Beihang University, Beijing 100191, China/ Beijing ...
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2012

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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.