Deep Learning Based Text Classification: A Comprehensive Review
Shervin Minaee Snapchat Inc , Nal Kalchbrenner Google Brain, Amsterdam , Erik Cambria Nanyang Technological University, Singapore , Narjes Nikzad University of Tabriz , Meysam Chenaghlu University of Tabriz and Jianfeng Gao Microsoft Research, Redmond
Abstract
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.
中文速览
文本分类是自然语言处理的核心任务，但面对海量非结构化文本，如何选择合适的深度学习模型一直让研究者和工程师无从下手。这篇综述系统梳理了近六年来150余个专为文本分类设计的深度学习模型，涵盖情感分析、新闻分类、问答、自然语言推理等主要任务，按照前馈网络、循环神经网络（RNN）、卷积神经网络（CNN）、注意力机制（Attention）、Transformer等架构分类逐一解析其技术原理与优劣。文章还整理了40余个常用基准数据集，并在16个公开榜单上对主流模型做了定量对比，清晰呈现了各类方法的性能差距。这项工作为想要快速了解深度学习文本分类全貌的读者提供了一份详尽的路线图，也为未来研究方向的讨论奠定了基础。
原文 arXiv:2004.03705;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2004.03705v3