An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

被引：204
|
作者：
Lauriola, Ivano [1 ,2 ]
Lavelli, Alberto [3 ]
Aiolli, Fabio [2 ]
机构：
[1] Amazon Alexa AI, Los Altos, CA USA
[2] Univ Padua, Dept Math, Padua, Italy
[3] Fdn Bruno Kessler, Trento, Italy
来源：
NEUROCOMPUTING | 2022年 / 470卷
关键词：
Deep Learning; Natural Language Processing; Transformer; Language Models; Software;
D O I：
10.1016/j.neucom.2021.05.103
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Natural Language Processing (NLP) is a branch of artificial intelligence that involves the design and implementation of systems and algorithms able to interact through human language. Thanks to the recent advances of deep learning, NLP applications have received an unprecedented boost in performance. In this paper, we present a survey of the application of deep learning techniques in NLP, with a focus on the various tasks where deep learning is demonstrating stronger impact. Additionally, we explore, describe, and revise the main resources in NLP research, including software, hardware, and popular corpora. Finally, we emphasize the main limits of deep learning in NLP and current research directions. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码：443 / 456
页数：14
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