A Survey of the Usages of Deep Learning for Natural Language Processing
| Autor: | Daniel W. Otter, Jugal Kalita, Julian Richard Medina |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Deep linguistic processing
Computer Networks and Communications Computer science 02 engineering and technology computer.software_genre Field (computer science) Deep Learning Computer Systems Artificial Intelligence Surveys and Questionnaires 0202 electrical engineering electronic engineering information engineering Humans Natural Language Processing Artificial neural network business.industry Deep learning Linguistics Computer Science Applications 020201 artificial intelligence & image processing Neural Networks Computer Artificial intelligence Computational linguistics business computer Software Natural language processing |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 32:604-624 |
| ISSN: | 2162-2388 2162-237X |
| DOI: | 10.1109/tnnls.2020.2979670 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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