Correlating medical-dependent query features with image retrieval models using association rules
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
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.