A Survey of the Usages of Deep Learning in Natural Language Processing

TL;DR

This survey reviews the rapid growth of deep learning applications in natural language processing, summarizing architectures, research areas, and future directions to guide ongoing and future research efforts.

Contribution

It provides a comprehensive overview of deep learning methods and their applications in NLP, highlighting recent advances and suggesting future research directions.

Findings

Deep learning has significantly advanced NLP capabilities.

Various architectures like RNNs, CNNs, and Transformers are widely used.

Future research should focus on interpretability and low-resource languages.

Abstract

Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.