Cai, Jingjing
(School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu)
,
Li, Jianping
(School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu)
,
Li, Wei
(School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu)
,
Wang, Ji
(College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China)
Text classification is one of the most widely used natural language processing technologies. Common text classification applications include spam identification, news text classification, information retrieval, emotion analysis, and intention judgment, etc. Traditional text classifiers based on mach...
Text classification is one of the most widely used natural language processing technologies. Common text classification applications include spam identification, news text classification, information retrieval, emotion analysis, and intention judgment, etc. Traditional text classifiers based on machine learning methods have defects such as data sparsity, dimension explosion and poor generalization ability, while classifiers based on deep learning network greatly improve these defects, avoid cumbersome feature extraction process, and have strong learning ability and higher prediction accuracy. For example, convolutional neural network (CNN)[I]. This paper introduces the process of text classification and focuses on the deep learning model used in text classification.
Text classification is one of the most widely used natural language processing technologies. Common text classification applications include spam identification, news text classification, information retrieval, emotion analysis, and intention judgment, etc. Traditional text classifiers based on machine learning methods have defects such as data sparsity, dimension explosion and poor generalization ability, while classifiers based on deep learning network greatly improve these defects, avoid cumbersome feature extraction process, and have strong learning ability and higher prediction accuracy. For example, convolutional neural network (CNN)[I]. This paper introduces the process of text classification and focuses on the deep learning model used in text classification.
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