计算机科学
人工智能
深度学习
推论
分类
答疑
自然语言处理
情绪分析
机器学习
作者
Shervin Minaee,Nal Kalchbrenner,Erik Cambria,Narjes Nikzad-Khasmakhi,Meysam Chenaghlu,Jianfeng Gao
摘要
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.
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