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

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 2; pp. 604 - 624
• Main Authors: Otter, Daniel W., Medina, Julian R., Kalita, Jugal K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational linguistics; Computer applications; Computer architecture; Computer Systems; Decoding; Deep Learning; Humans; Language; Linguistics; Machine learning; Microprocessors; Natural Language Processing; natural language processing (NLP); Neural networks; Neural Networks, Computer; Surveys and Questionnaires; Training
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.2979670

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 article 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 many 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.