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计算机系统应用:2018,27(9):157-162
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基于Bi-LSTM和CNN并包含注意力机制的社区问答问句分类方法
史梦飞1, 杨燕1, 贺樑1, 陈成才2
(1.华东师范大学 计算机科学与软件工程学院, 上海 200062;2.上海智臻智能网络科技股份有限公司, 上海 201803)
Question Categorization of Community Question Answering by Combining Bi-LSTM and CNN with Attention Mechanism
SHI Meng-Fei1, YANG Yan1, HE Liang1, CHEN Cheng-Cai2
(1.School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China;2.Xiaoi Robot Technology Co. Ltd., Shanghai 201803, China)
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投稿时间:2018-01-20    修订日期:2018-02-09
中文摘要: 问句分类的目标是将用户提出的自然语言问句分到预先设定的类别.在社区问答中,如何准确高效的对问句进行分类是一项重要任务.本文提出了一种基于深度神经网络的问句分类方法,该方法首先将问句用词向量进行表示,然后用融合双向长短时记忆网络(Bi-LSTM)和卷积神经网络(CNN)结构并包含注意力机制的深度学习模型提取问句特征进行分类.该方法的特色在于利用Bi-LSTM和CNN在句子级文本表示的优点,充分捕捉问句特征,并结合问句的对应答案来表示问句,丰富了问句信息.实验表明,该问句分类方法准确率较高,在多个数据集上取得不错结果.
Abstract:The goal of question categorization is to classify natural language questions that user raised into predefined categories. How to classify question sentences accurately and efficiently is an important task in community question answering. In this study, we propose a question categorization method based on deep neural network. Firstly, the words of the question are transformed to vectors. Then, we use a novel Bidirectional Long Short-Term Memory (Bi-LSTM) based Convolutional Neural Network (CNN) model with attention mechanism to capture the most important features in a question. Finally, the features are fed into the classifier to predict the category of the question. We use the Bi-LSTM and CNN to capture the features of question because of their benefits in representing sentence level documents. We also use the answer set to enrich the information of the question. The experimental results on several datasets demonstrate the effectiveness of the proposed approach.
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基金项目:上海市经济和信息化委员会项目(201602024);上海市科学技术委员会项目(14DZ2260800)
引用文本:
史梦飞,杨燕,贺樑,陈成才.基于Bi-LSTM和CNN并包含注意力机制的社区问答问句分类方法.计算机系统应用,2018,27(9):157-162
SHI Meng-Fei,YANG Yan,HE Liang,CHEN Cheng-Cai.Question Categorization of Community Question Answering by Combining Bi-LSTM and CNN with Attention Mechanism.COMPUTER SYSTEMS APPLICATIONS,2018,27(9):157-162

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