Abstract
Search is important in medical domain. For example, physicians need to search for literature to support their decisions when they diagnose the patients, especially for the complicated cases. Even though they could manually input the queries, it is not an easy task because queries are expected to include enough information about the patients. Therefore, the queries tend to be verbose. However, those verbose queries may not work well since the search engine would favor documents covering every term in the query, but not the ones which are truly important. Existing work on verbose query processing in Web search has studied the similar problem, but the methods are not applicable to the medical domain because of the complexity of the medical queries and the lack of domain-specific features. In this work, we propose a set of new features to capture the importance of the terms which are helpful for medical retrieval, i.e., Key Terms, from verbose queries. Experiment results on the TREC Clinical Decision Support collections show that the improvement of using the selected Key Terms over the baseline methods is statistically significant.
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This research was supported by the U.S. National Science Foundation under IIS-1423002.
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Wang, Y., Lu, K., Fang, H. (2017). Learning2extract for Medical Domain Retrieval. In: Sung, WK., et al. Information Retrieval Technology. AIRS 2017. Lecture Notes in Computer Science(), vol 10648. Springer, Cham. https://doi.org/10.1007/978-3-319-70145-5_4
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DOI: https://doi.org/10.1007/978-3-319-70145-5_4
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