Question Recommendation in Medical Community-Based Question Answering

摘要

The medical community question answering system (MCQA) which is a new kind of medical information exchange platform is becoming more and more popular. Due to the number of patients is much more than the doctors, resulting in many patients can not get timely answers to their questions. Similar question recommendation is a common approach to solve this problem. The contributions of this paper are two-fold: (1) we propose a Siamese CNN model which measure correlation between questions and answers. (2) We first apply word2vec to learn the semantic relations between words and then construct a similar question retrieval model with answers. The study above can achieve a good performance in the real MCQA data set. It shows that our method can effectively extract similar questions recommendation list, shorten user's time to wait for em answer and improve user experience as well.