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Deep Learning-based Text Classification:A Comprehensive Review - 2021

Research Area:  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 article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we 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 we discuss future research directions.

Author(s) Name:  Shervin Minaee , Nal Kalchbrenner , Erik Cambria , Narjes Nikzad , Meysam Chenaghlu , Jianfeng Gao

Journal name:  ACM Computing Surveys

Conferrence name:  

Publisher name:  ACM

DOI:  10.1145/3439726

Volume Information:  Volume 54,Issue 3,April 2022, Article No.: 62,pp 1–40