Representation Learning for Natural Language Processing

Representation Learning for Natural Language Processing

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This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniq...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Liu, Zhiyuan (Editor), Lin, Yankai (Editor), Sun, Maosong (Editor)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edition:2nd ed. 2023.
Subjects:
Natural language processing (Computer science).
Computational linguistics.
Artificial intelligence.
Data mining.
Natural Language Processing (NLP).
Computational Linguistics.
Artificial Intelligence.
Data Mining and Knowledge Discovery.
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Table of Contents:
  • Chapter 1. Representation Learning and NLP
  • Chapter 2. Word Representation
  • Chapter 3. Compositional Semantics
  • Chapter 4. Sentence Representation
  • Chapter 5. Document Representation
  • Chapter 6. Sememe Knowledge Representation
  • Chapter 7. World Knowledge Representation
  • Chapter 8. Network Representation
  • Chapter 9. Cross-Modal Representation
  • Chapter 10. Resources
  • Chapter 11. Outlook.

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