Deeplearning Model Used in Text Classification

Jingjing Cai, Jianping Li, Wei Li, Ji Wang
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引用次数: 43

Abstract

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.
用于文本分类的深度学习模型
文本分类是应用最广泛的自然语言处理技术之一。常见的文本分类应用包括垃圾邮件识别、新闻文本分类、信息检索、情感分析和意图判断等。传统的基于机器学习方法的文本分类器存在数据稀疏、维度爆炸、泛化能力差等缺陷,而基于深度学习网络的分类器极大地改善了这些缺陷,避免了繁琐的特征提取过程,学习能力强,预测精度更高。例如卷积神经网络(CNN)[1]。本文介绍了文本分类的过程,重点介绍了文本分类中使用的深度学习模型。
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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