skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Representation Learning for Natural Language Processing

ISBN: 9789819916009 ;ISBN: 9819916003 ;ISBN: 9819915996 ;ISBN: 9789819915996 ;DOI: 10.1007/978-981-99-1600-9

Digital Resources/Online E-Resources

Citations Cited by
  • Title:
    Representation Learning for Natural Language Processing
  • Author: Lin, Yankai ; Liu, Zhiyuan ; Sun, Maosong
  • Subjects: Artificial intelligence ; Big Data ; Book Industry Communication ; Computational linguistics ; Computer science ; Computing & information technology ; Data mining ; Deep Learning ; Document Representation ; Knowledge Representation ; Language ; linguistics ; Machine Learning ; Natural language & machine translation ; Natural Language Processing ; Representation Learning ; Word Representation
  • Description: 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.
  • Publisher: Singapore: Springer Nature
  • Creation Date: 2023
  • Format: 521
  • Language: English
  • Identifier: ISBN: 9789819916009
    ISBN: 9819916003
    ISBN: 9819915996
    ISBN: 9789819915996
    DOI: 10.1007/978-981-99-1600-9
  • Source: OAPEN
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait