Description
Semantics and Deep Learning offers a thorough examination of how deep learning methods are revolutionizing the study of linguistic meaning. The book explores the theoretical foundations of semantics while investigating how neural networks learn to represent and process semantic information.
The authors discuss key topics including word embeddings, neural language models, and semantic composition in neural systems. They analyze how deep learning approaches differ from traditional symbolic semantic frameworks and what insights neural models provide about human language understanding.
This Element bridges the gap between classical semantic theory and contemporary machine learning, making it essential reading for linguists, computer scientists, and researchers interested in natural language processing. The book combines mathematical rigor with accessibility, demonstrating practical applications while maintaining theoretical depth.
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