國立虎尾科技大學 |

Representation learning for natural language processing

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Representation learning for natural language processing/ edited by Zhiyuan Liu, Yankai Lin, Maosong Sun.
other author:	Liu, Zhiyuan.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xx, 521 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science) -
Online resource:	https://doi.org/10.1007/978-981-99-1600-9
ISBN:	9789819916009

Representation learning for natural language processing
Representation learning for natural language processing[electronic resource] /edited by Zhiyuan Liu, Yankai Lin, Maosong Sun. - Second edition. - Singapore :Springer Nature Singapore :2023. - xx, 521 p. :ill., digital ;24 cm.

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.

Open access.

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.

ISBN: 9789819916009

Standard No.: 10.1007/978-981-99-1600-9doiSubjects--Topical Terms:

641811
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Representation learning for natural language processing
LDR:03348nam a2200349 a 4500 001 1115869
003 DE-He213
005 20230823043640.0
006 m d
007 cr nn 008maaau
008 240123s2023 si s 0 eng d
020 $a 9789819916009 $q (electronic bk.)
020 $a 9789819915996 $q (paper)
024 7 $a 10.1007/978-981-99-1600-9 $2 doi
035 $a 978-981-99-1600-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.N38
072 7 $a UYQL $2 bicssc
072 7 $a COM073000 $2 bisacsh
072 7 $a UYQL $2 thema
082 0 4 $a 006.35 $2 23
090 $a QA76.9.N38 $b R425 2023
245 0 0 $a Representation learning for natural language processing $h [electronic resource] / $c edited by Zhiyuan Liu, Yankai Lin, Maosong Sun.
250 $a Second edition.
260 $a Singapore : $c 2023. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xx, 521 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
506 $a Open access.
520 $a 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.
650 0 $a Natural language processing (Computer science) $3 641811
650 0 $a Computational linguistics. $3 555811
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data mining. $3 528622
650 1 4 $a Natural Language Processing (NLP) $3 1211064
650 2 4 $a Computational Linguistics. $3 670080
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Liu, Zhiyuan. $e editor. $3 1255436
700 1 $a Lin, Yankai. $e author. $3 1313378
700 1 $a Sun, Maosong. $3 1069420
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-1600-9
950 $a Computer Science (SpringerNature-11645)