Biomedical Question Answering: A Comprehensive Review

• URL: http://arxiv.org/abs/2102.05281v1
• Date: Wed, 10 Feb 2021 06:16:35 GMT
• Title: Biomedical Question Answering: A Comprehensive Review
• Authors: Qiao Jin, Zheng Yuan, Guangzhi Xiong, Qianlan Yu, Chuanqi Tan, Mosha Chen, Songfang Huang, Xiaozhong Liu, Sheng Yu
• Abstract summary: Question Answering (QA) is a benchmark Natural Language Processing (NLP) task where models predict the answer for a given question using related documents, images, knowledge bases and question-answer pairs. For specific domains like biomedicine, QA systems are still rarely used in real-life settings. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge.
• Score: 19.38459023509541
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question Answering (QA) is a benchmark Natural Language Processing (NLP) task where models predict the answer for a given question using related documents, images, knowledge bases and question-answer pairs. Automatic QA has been successfully applied in various domains like search engines and chatbots. However, for specific domains like biomedicine, QA systems are still rarely used in real-life settings. Biomedical QA (BQA), as an emerging QA task, enables innovative applications to effectively perceive, access and understand complex biomedical knowledge. In this work, we provide a critical review of recent efforts in BQA. We comprehensively investigate prior BQA approaches, which are classified into 6 major methodologies (open-domain, knowledge base, information retrieval, machine reading comprehension, question entailment and visual QA), 4 topics of contents (scientific, clinical, consumer health and examination) and 5 types of formats (yes/no, extraction, generation, multi-choice and retrieval). In the end, we highlight several key challenges of BQA and explore potential directions for future works.

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