Review of Text Classification Methods on Deep Learning |
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Authors: | Hongping Wu Yuling Liu Jingwen Wang |
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Affiliation: | 1.College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
2 Department of Computer Science, Elizabethtown College, PA, 17022, USA. |
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Abstract: | Text classification has always been an increasingly crucial topic in natural
language processing. Traditional text classification methods based on machine learning
have many disadvantages such as dimension explosion, data sparsity, limited generalization
ability and so on. Based on deep learning text classification, this paper presents an
extensive study on the text classification models including Convolutional Neural
Network-Based (CNN-Based), Recurrent Neural Network-Based (RNN-based), Attention
Mechanisms-Based and so on. Many studies have proved that text classification methods
based on deep learning outperform the traditional methods when processing large-scale and
complex datasets. The main reasons are text classification methods based on deep learning
can avoid cumbersome feature extraction process and have higher prediction accuracy for a
large set of unstructured data. In this paper, we also summarize the shortcomings of
traditional text classification methods and introduce the text classification process based on
deep learning including text preprocessing, distributed representation of text, text
classification model construction based on deep learning and performance evaluation. |
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Keywords: | Text classification deep learning distributed representation CNN RNN attention mechanism |
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