Representation learning for natural language processing / Zhiyuan Liu, Yankai Lin, Maosong Sun, editors.
Liu, Zhiyuan, 1984- editor.; Lin, Yankai editor.; Sun, Maosong, editor.
2023
QA76.9.N38
Available Online

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Title Representation learning for natural language processing / Zhiyuan Liu, Yankai Lin, Maosong Sun, editors.
Edition Second edition.
ISBN 9789819916009 (electronic bk.)
9819916003 (electronic bk.)
9789819915996
DOI https://doi.org/10.1007/978-981-99-1600-9
Published Singapore : Springer, 2023.
Language English
Description 1 online resource (xx, 521 pages) : color illustrations
Item Number 10.1007/978-981-99-1600-9 doi
Call Number QA76.9.N38
Dewey Decimal Classification 006.3/5
Summary 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 techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing. As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book.
Access Note Open access.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed August 31, 2023).
Added Author Liu, Zhiyuan, 1984- editor.
Lin, Yankai editor.
Sun, Maosong, editor.
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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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