论文标题
自然语言处理通过深度学习的进步：调查
Natural Language Processing Advancements By Deep Learning: A Survey
论文作者
论文摘要
自然语言处理（NLP）通过增强对基于语言的人类计算机沟通的人类语言的更好理解，从而有助于增强智能机器的能力。计算能力的最新发展以及大量语言数据的出现增强了使用数据驱动方法自动化语义分析的需求和需求。由于通过在计算机视觉，自动语音识别（尤其是NLP）等领域中使用深度学习方法所表现出的重大改进，因此数据驱动策略的利用现在普遍存在。这项调查对NLP的不同方面和应用进行了分类，这些方面和应用从深度学习中受益。它涵盖了NLP核心任务和应用程序，并描述了深度学习方法和模型如何发展这些领域。我们进一步分析和比较不同的方法和最先进的模型。
Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.
