Deep Learning Based Text Classification: A Comprehensive Review
Computation and Language
2021-01-05 v3 Machine Learning
Machine Learning
Abstract
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this paper, we provide a comprehensive review of more than 150 deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and discuss future research directions.
Cite
@article{arxiv.2004.03705,
title = {Deep Learning Based Text Classification: A Comprehensive Review},
author = {Shervin Minaee and Nal Kalchbrenner and Erik Cambria and Narjes Nikzad and Meysam Chenaghlu and Jianfeng Gao},
journal= {arXiv preprint arXiv:2004.03705},
year = {2021}
}