  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.
