Recognizing Question Entailment for Medical Question Answering
Recognizing Question Entailment for Medical Question Answering
复制标题
识别医疗问答的问题内涵
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Dina Demner
中科院分区：
文献类型：
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作者：
Asma Ben Abacha;Dina Demner
随着医学文本的异质性和专业化程度的提高，自动问答变得越来越具有挑战性。在这种情况下，通过检索人类专家已经回答的类似问题来回答给定的医学问题似乎是一个有前途的解决方案。在本文中，我们提出了一种新的方法来检测相似的问题的基础上识别问题蕴涵（RQE）。特别是，我们认为常见问题（FAQ）是一个有价值的和广泛的信息来源。我们的最终目标是，如果存在类似于消费者健康问题的FAQ，则自动提供现有答案。我们使用美国国家医学图书馆收到的消费者健康问题和从NIH网站收集的常见问题来评估我们的方法。我们的第一个结果是有希望的，并建议我们的方法作为一个有价值的补充经典的问答方法的可行性。
With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we propose a new approach for the detection of similar questions based on Recognizing Question Entailment (RQE). In particular, we consider Frequently Asked Question (FAQs) as a valuable and widespread source of information. Our final goal is to automatically provide an existing answer if FAQ similar to a consumer health question exists. We evaluate our approach using consumer health questions received by the National Library of Medicine and FAQs collected from NIH websites. Our first results are promising and suggest the feasibility of our approach as a valuable complement to classic question answering approaches.