论文标题
基于深度学习的文本分类：全面评论
Deep Learning Based Text Classification: A Comprehensive Review
论文作者
论文摘要
基于深度学习的模型已超过了各种文本分类任务中基于机器学习的方法，包括情感分析，新闻分类，问答和自然语言推断。在本文中，我们对近年来开发的150多个基于深度学习的文本分类模型进行了全面综述，并讨论了它们的技术贡献，相似性和优势。我们还提供了40多个流行数据集的摘要，用于文本分类。最后，我们对流行基准的不同深度学习模型的性能进行定量分析，并讨论未来的研究方向。
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.
