Text Classification of Mixed Model Based on Deep Learning

IF 0.7 Q3 ENGINEERING, MULTIDISCIPLINARY
Sang-Hwa Lee
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引用次数: 0

Abstract

At present, deep learning has been widely used many fields, but the research on text classification is still relatively few. This paper makes full use of the good learning characteristics of deep learning, proposes a hybrid model based on deep learning, and designs a text classifier based on the hybrid model. This hybrid model uses two common deep learning models, sparse automatic encoder and deep confidence network, to mix. The hybrid model is mainly composed of three parts, the first two layers are constructed by sparse automatic encoder, the middle layer is a three-layer depth Convolutional Neural Network (CNN), and finally Softmax regression is used as the classification layer. In order to test the classification performance of the classifier based on deep learning hybrid model, relevant experiments were conducted on English data set 20Newsgroup and Chinese data set Fudan University Chinese Corpus. In the English text classification experiment, the classifier based on deep learning hybrid model is used to classify, and a high classification accuracy rate is obtained. In order to further verify the superiority of its performance, a comparative experiment with naive Bayes classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier demonstrates that the classification effect of the classifier based on deep learning hybrid model is better than that of naive Bayes classifier, KNN classifier and support vector machine classifier. In the experiment of Chinese text classification, the Chinese corpus of Fudan University is tested, and a good classification effect is obtained. The influence of different parameter settings on the classification accuracy is discussed.
基于深度学习的混合模型文本分类
目前,深度学习已经在很多领域得到了广泛的应用,但是对于文本分类的研究还比较少。本文充分利用深度学习良好的学习特性,提出了一种基于深度学习的混合模型,并设计了基于混合模型的文本分类器。该混合模型采用稀疏自动编码器和深度置信网络两种常见的深度学习模型进行混合。混合模型主要由三部分组成,前两层由稀疏自动编码器构建,中间层为三层深度卷积神经网络(CNN),最后采用Softmax回归作为分类层。为了测试基于深度学习混合模型的分类器的分类性能,分别在英文数据集20Newsgroup和中文数据集复旦大学中文语料库上进行了相关实验。在英语文本分类实验中,采用基于深度学习混合模型的分类器进行分类,获得了较高的分类准确率。为了进一步验证其性能的优越性,通过与朴素贝叶斯分类器、k -最近邻(KNN)分类器和支持向量机(SVM)分类器的对比实验表明,基于深度学习混合模型的分类器分类效果优于朴素贝叶斯分类器、KNN分类器和支持向量机分类器。在中文文本分类实验中,对复旦大学中文语料库进行了测试,取得了良好的分类效果。讨论了不同参数设置对分类精度的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
TEHNICKI GLASNIK-TECHNICAL JOURNAL
TEHNICKI GLASNIK-TECHNICAL JOURNAL ENGINEERING, MULTIDISCIPLINARY-
CiteScore
1.50
自引率
8.30%
发文量
85
审稿时长
15 weeks
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