A Shallow Neural Network Based Short Text Classifier For Medical Community Question Answering System

The medical community question answering system (MCQA) which is a novel medical communicating platform is becoming popular due to the large amount of people seeking for solution of health problem. Not all medical questions would get timely answers. Similar question recommendation is a common approach to solve this problem. While the large amount of Q&A pairs in MCQA, classifying for a new problem effectively reduces the size of candidate problem set and improve the efficiency of retrieval for its similar problems. In this paper we proposed a shallow, CNN classification model (SCCM) based on characters which not only narrowing the text feature vector dimensions, but also solving spelling mistakes in text. We crawls and constructs a real Chinese medical Q&A dataset and conducts experimental verification. The results of experiments show that our SCCM can automatically extract the key features in the text and improve the accuracy of classification.