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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Bioinformatics

Open Access Proceedings

Modeling and mining term association for improving biomedical information retrieval performance

Qinmin Hu12, Jimmy Xiangji Huang13* and Xiaohua Hu4

Author Affiliations

1 Information Retrieval and Knowledge Management Research Lab, York University, Toronto, ON, M3J1P3, Canada

2 Department of Computer Science & Engineering, York University, Toronto, ON, M3J1P3, Canada

3 School of Information Technology, York University, Toronto, ON, M3J1P3, Canada

4 College of Information Science and Technology, Drexel University, Philadelphia, PA, 19104, USA

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BMC Bioinformatics 2012, 13(Suppl 9):S2  doi:10.1186/1471-2105-13-S9-S2

Published: 11 June 2012

Abstract

Background

The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance.

Results

We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10.

Conclusions

First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.