医学领域中基于注意力机制的查询扩展

临床决策支持系统中，通常使用电子病历中的病人描述作为查询检索，进而辅助医生做决策分析.我们提出了一个基于注意力机制的网络扩展查询方法以提高检索效果.由于医学文本注释的难度和成本很高，并受到了迁移学习理念的启发，我们选择了非医学领域数据集学习句子与实体的关系，迁移到医学领域数据集，模型用LSTM获得句子表征并用注意力机制来获得实体表示.我们提出的方法可以动态选择相关实体作为查询扩展，同时我们不仅考虑单个实体作为扩展的影响，也考虑了实体组合作为扩展的影响，解决了选择固定数目实体的问题.我们在TREC Clinical Decision Support Track三个标准数据集上进行实验，实验表明本文提出的方法在实验结果上有显著的提升.

Attention Based Network for Query Expansion in Medical Domain

The aim of clinical decision support implementing electronic health records is to satisfy the physicians'' information needs. We are motivated to propose an attention based network on query expansion. Considering the difficulty and cost of medical text annotation and inspired by the idea of migration learning, we chose the non-medical dataset for model training, and migrated to medical datasets. The model utilizes LSTM to obtain sentence representation and adopt attention mechanism to obtain entities representation. The proposed approach can dynamically select related entities as expansion of the query. At the same time, we not only consider the score of a single term as an expansion term, but also consider the score of term combination. We conduct the experiments on the three standard datasets of TREC Clinical Decision Support Track, where the approach has a promising overall performance over the strong baseline.