摘要
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.
基金
This work supported in part by the National Natural Science Foundation of China under Grant 61872134,in part by the Natural Science Foundation of Hunan Province under Grant 2018JJ2062
in part by Science and Technology Development Center of the Ministry of Education under Grant 2019J01020
in part by the 2011 Collaborative Innovative Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province。
