A Review of Text Classification Based on Deep Learning

Yifan Zhou
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引用次数: 18

Abstract

Text classification is the process of discriminating predetermined text into a certain class or some certain classes. Text categorization has important applications in redundant filtering, organization management, information retrieval, index building, ambiguity resolution, and text filtering. This paper we will mainly introduce the research background of text classification and tracks the research dynamics of text classification at home and abroad. Text classification is an essential component in many NLP problems. Neural network model had achieved extraordinary effect in text classification. So we will discuss how the general methods with deep learning to deal with text classification problems, including Convolution Neural Network(CNN), Recurrent convolution neural network(RCNN), Long Short-Term memory(LSTM), and fastCNN. CNN can construct the representation of text using a convolutional neural network. RNN does well in capturing contextual information. LSTM is explicitly designed for time-series data for learning long-term dependencies. Besides, we will introduce the distributed representation, such as Continuous Bags of Words(CBOW) and Skip-Gram. And analyze the advantages of word2vec model over on-hot encoding.
基于深度学习的文本分类研究综述
文本分类是将预先确定的文本区分为某一类或某些特定类别的过程。文本分类在冗余过滤、组织管理、信息检索、索引建立、歧义消解和文本过滤等方面有着重要的应用。本文主要介绍了文本分类的研究背景,并对国内外文本分类的研究动态进行了跟踪。文本分类是许多自然语言处理问题的重要组成部分。神经网络模型在文本分类中取得了非凡的效果。因此,我们将讨论如何使用深度学习的一般方法来处理文本分类问题,包括卷积神经网络(CNN)、循环卷积神经网络(RCNN)、长短期记忆(LSTM)和快速CNN。CNN可以使用卷积神经网络来构建文本的表示。RNN在获取上下文信息方面做得很好。LSTM是明确为时间序列数据设计的,用于学习长期依赖关系。此外,我们将介绍分布式表示,如连续词袋(CBOW)和Skip-Gram。并分析了word2vec模型相对于非热编码的优点。
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