Biomedical Question Answering: A Survey of Approaches and Challenges

被引:46
|
作者
Jin, Qiao [1 ]
Yuan, Zheng [1 ]
Xiong, Guangzhi [1 ]
Yu, Qianlan [1 ]
Ying, Huaiyuan [1 ]
Tan, Chuanqi [2 ]
Chen, Mosha [2 ]
Huang, Songfang [2 ]
Liu, Xiaozhong [3 ]
Yu, Sheng [1 ]
机构
[1] Tsinghua Univ, Beijing, Peoples R China
[2] Alibaba Grp, Hangzhou, Peoples R China
[3] Indiana Univ, Bloomington, IN 47405 USA
关键词
Question answering; natural language processing; machine learning; biomedicine; CLINICAL QUESTIONS; LINKED DATA; SYSTEM; TEXT; MODEL;
D O I
10.1145/3490238
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Automatic Question Answering (QA) has been successfully applied in various domains such as search engines and chatbots. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access, and understand complex biomedical knowledge. There have been tremendous developments of BQA in the past two decades, which we classify into five distinctive approaches: classic, information retrieval, machine reading comprehension, knowledge base, and question entailment approaches. In this survey, we introduce available datasets and representative methods of each BQA approach in detail. Despite the developments, BQA systems are still immature and rarely used in real-life settings. We identify and characterize several key challenges in BQA that might lead to this issue, and we discuss some potential future directions to explore.
引用
收藏
页数:36
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