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:2019,28(7):145-150
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针对文本分类的神经网络模型
(1.杭州师范大学 信息科学与工程学院, 杭州 311121;2.
移动健康管理系统教育部工程研究中心, 杭州 311121)
Neural Network Models for Text Classification
(1.School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China;2.
Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou 311121, China)
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投稿时间:2019-01-05    修订日期:2019-01-24
中文摘要: 文本分类是自然语言处理领域的一项重要任务,具有广泛的应用场景,比如知识问答、文本主题分类、文本情感分析等.解决文本分类任务的方法有很多,如支持向量机(Support Vector Machines,SVM)模型和朴素贝叶斯(Naïve Bayes)模型,现在被广泛使用的是以循环神经网络(Recurrent Neural Network,RNN)和文本卷积网络(TextConventional Neural Network,TextCNN)为代表的神经网络模型.本文分析了文本分类领域中的序列模型和卷积模型,并提出一种组合序列模型和卷积模型的混合模型.在公开数据集上对不同模型进行性能上的对比,验证了组合模型的性能要优于单独的模型.
Abstract:Text classification is an important task in the field of natural language processing. It has a wide range of applications, such as knowledge question and answer, text topic classification, text emotion analysis, and so on. There are many methods to solve the task of text classification, such as Support Vector Machines (SVM) model and Naïve Bayes model. Typical neural network models widely used now are the Recurrent Neural Network (RNN) and the Text Conventional Neural Network (TextCNN). In this study, the sequence model and convolution model in the field of text classification are analyzed, and a hybrid model of combining sequence model and convolution model is proposed. By comparing the performance of the different models on the open dataset, it is proved that the performance of the combined model is better than that of the single model.
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基金项目:浙江省自然科学基金(LQ16H180004);杭州市科技计划项目(20162013A02)
引用文本:
涂文博,袁贞明,俞凯.针对文本分类的神经网络模型.计算机系统应用,2019,28(7):145-150
TU Wen-Bo,YUAN Zhen-Ming,YU Kai.Neural Network Models for Text Classification.COMPUTER SYSTEMS APPLICATIONS,2019,28(7):145-150

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