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Selecting the most helpful answers in online health question answering communities

机译：选择在线健康问题回答社区中最有帮助的答案

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摘要

The online question answering (QA) community has been popular in recent years. In this paper, we focus on the online health question answering (HQA) community. The HQA community provides a platform for health consumers to inquire about health information. There are two ways to use this platform. One is to post a question and wait for answers to be provided by authenticated doctors. The other is to search for relevant questions with answers. For the latter, health consumers may prefer an accepted answer marked by the previous health consumer. However, there is a large proportion of questions without an accepted answer and it is inconvenient for people who want to search for relevant questions. To address this issue, we aim to select high-quality answers from the answers without marked accepted answers. We propose a deep learning approach to achieve this goal. To train the model for the prediction of answer quality, we first view the accepted answer as the positive answer and propose a method to label the negative answer. Next, we capture the semantic information on the question and the answer by the deep learning structure. We then combine the information to predict the quality score of the answer. We collect data from one of the biggest Chinese HQA community and divide them into groups by the medical departments for detailed analysis. Finally, we conduct experiments to show the effectiveness of categorization and the labeling method. The results show that our approach outperforms other studies and we further research into the differences among the results of different categories.
机译：近年来，在线问题回答（QA）社区一直受欢迎。在本文中，我们专注于在线健康问题应答（HQA）社区。 HQA社区为健康消费者提供了咨询健康信息的平台。有两种方法可以使用这个平台。一个是发布一个问题并等待经过身份验证的医生提供的答案。另一个是使用答案搜索相关问题。对于后者，健康消费者可能更喜欢以前的健康消费者标志着的接受答案。但是，没有公认的答案，有很大的问题，对于想要寻找相关问题的人来说是不方便的。为了解决这个问题，我们的目标是从答案中选择高质量的答案，而无需标记已接受的答案。我们提出了一种深入的学习方法来实现这一目标。要培训模型，以便预测答案质量，我们首先将接受的答案视为正答案，并提出了一种标记否定答案的方法。接下来，我们通过深入学习结构捕获关于问题的语义信息和答案。然后，我们将信息结合起来预测答案的质量得分。我们从最大的中国人HQA社区收集数据，并将其分成医疗部门的团体，以进行详细分析。最后，我们进行实验表明分类的有效性和标记方法。结果表明，我们的方法优于其他研究，我们进一步研究了不同类别结果的差异。

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