Abstract—Automatically determining similar questions and ranking the obtained questions according to their similarities to each input question is a very important task to any community Question Answering system (cQA). Various methods have applied for this task including conventional machine learning methods with feature extraction and some recent studies using deep learning methods. This paper addresses the problem of how to combine advantages of different methods into one unified model. Moreover, deep learning models are usually only effective for large data, while training data sets in cQA problems are often small, so the idea of integrating external knowledge into deep learning models for this cQA problem becomes more important. To this objective, we propose a neural network-based model which combines a Convolutional Neural Network (CNN) with features from other methods so that the deep learning model is enhanced with addtional knowledge sources. In our proposed model, the CNN component will learn the representation of two given questions, then combined additional features through a Multilayer Perceptron (MLP) to measure similarity between the two questions. We tested our proposed model on the SemEval 2016 task-3 data set and obtain better results in comparison with previous studies on the same task.
Index Terms—Community based question answering, convolutional neural networks, combining multiple sources.
Van-Tu Nguyen and Ha-Nam Nguyen are with the VNU University of Engineering and Technology, Ha Noi City, Vietnam (e-mail: tuspttb@gmail.com, namnh@vnu.edu.vn).
Anh-Cuong Le is with the Natural Language Processing and Knowledge Discovery Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam (e-mail: leanhcuong@tdtu.edu.vn).
Cite: Van-Tu Nguyen, Anh-Cuong Le, and Ha-Nam Nguyen, "A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 194-201, 2021.
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