Deep Learning and Linguistic Representation

The application of deep learning methods to natural language processing has achieved significant progress, but the implications for modeling human learning and representation are unclear. Deep Learning and Linguistic Representation explores the application of deep learning systems to NLP tasks and their impact on understanding human linguistic knowledge.

Format: Paperback / softback
Length: 168 pages
Publication date: 27 April 2021
Publisher: Taylor & Francis Ltd

The application of deep learning methods to problems in natural language processing has brought about remarkable progress across a diverse array of natural language processing tasks. In certain instances, deep learning models have achieved or even surpassed human performance, marking a significant milestone in the field of artificial intelligence. While the success of this approach has revolutionized the engineering methods of machine learning in artificial intelligence, the implications of these achievements for modeling human learning and representation remain somewhat unclear.

Deep Learning and Linguistic Representation delves into the exploration of various deep learning systems applied to cognitively captivating NLP tasks. It also examines the extent to which this research sheds light on our understanding of how humans acquire and represent linguistic knowledge.

Key Features:

Combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics.

Offers a self-contained and suitable teaching resource for computer science, AI, and cognitive science courses, without requiring extensive technical training in these domains.

Provides a concise guide to working on state-of-the-art systems that are driving a revolutionary transformation across a wide range of challenging natural language tasks.

Weight: 254g
Dimension: 155 x 233 x 13 (mm)
ISBN-13: 9780367648749