Towards a Better Ranking for Biomedical Information Retrieval Using Context

In this paper, we present a context-sensitive approach for re-ranking retrieved documents for further improving the effectiveness of high-performance biomedical literature retrieval systems. For each topic, a two-dimensional context is learnt from the top N and the last N´ documents in initial retrieval ranked list, which 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 two strong biomedical literature retrieval runs demonstrates that the context-sensitive re-ranking approach yields better retrieval performance.