Key Terms Guided Expansion for Verbose Queries in Medical Domain

Abstract

Due to the complex nature of medical concepts and information need, the queries tend to be verbose in medical domain. Verbose queries lead to sub-optimal performance since the current search engine promotes the results covering every query term, but not the truly important ones. Key term extraction has been studied to solve this problem, but another problem, i.e., vocabulary gap between query and documents, need to be discussed. Although various query expansion techniques have been well studied for the vocabulary gap problem, existing methods suffer different drawbacks such as inefficiency and expansion term mismatch. In this work, we propose to solve this problem by following the intuition that the surrounding contexts of the important terms in the original query should also be essential for retrieval. Specifically, we first identify the key terms from the verbose query and then locate the contexts of these key terms in the original document collection. The terms in the contexts are weighted and aggregated to select the expansion terms. We conduct experiments with five TREC data collections using the proposed methods. The results show that the improvement of the retrieval performance of proposed method is statistically significant comparing with the baseline methods.