基于混合神经网络的问题分类方法

自动问答系统对用户自然语言方式提出的问题，给出快速准确的答案，引起了学术界与工业界的广泛关注。问题分类任务通过自动判断问题类型，对提高问答系统回答问题的准确率具有重要意义。本文利用问题和答案的上下文信息，结合卷积神经网络和循环神经网络各自的优势，提出一种混合深度学习模型。除此之外，为了增强问题特征的表达能力，该模型引入注意力机制，提升模型的泛化能力。在360问答数据集进行对比实验验证，实验表明，本文模型相比于传统方法提升了1.6%~5.6%。

Question Classification Based on Hybrid Neural Network Model

The automatic question answering system gives fast and accurate answers to the questions proposed by the users in natural language, arousing widespread concern in academia and industry. By automatically determining the type of question, question classification task is of great significance to improve the accuracy of the question answering system. Based on the contextual information of the question and answer, combined with the respective advantages of convolutional neural networks and recurrent neural networks, this paper proposes a hybrid deep learning model. In addition, in order to strengthen the representation capacity of the question, this model adopts attention mechanism and enhances the generalization ability of the model. In this paper, we conduct a comparative experiment on 360 QA datasets, results show that this model has improved 1.6%~5.6% compared with the traditional method.