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Natural Language Processing
A Machine Learning Perspective
Buch von Yue Zhang (u. a.)
Sprache: Englisch

59,25 €*

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Beschreibung
With a machine learning approach and less focus on linguistic details, this natural language processing textbook introduces the fundamental mathematical and deep learning models for NLP in a unified framework. An invaluable, accessible and up-to-date tool for the upper undergraduate and graduate student, with sample code available online.
With a machine learning approach and less focus on linguistic details, this natural language processing textbook introduces the fundamental mathematical and deep learning models for NLP in a unified framework. An invaluable, accessible and up-to-date tool for the upper undergraduate and graduate student, with sample code available online.
Über den Autor
Yue Zhang is an associate professor at Westlake University. Before joining Westlake, he worked as a research associate at the University of Cambridge and then a faculty member at Singapore University of Technology and Design. His research interests lie in fundamental algorithms for NLP, syntax, semantics, information extraction, text generation, and machine translation. He serves as an action editor for TACL, and as area chairs of ACL, EMNLP, COLING, and NAACL. He gave several tutorials at ACL, EMNLP and NAACL, and won a best paper award at COLING in 2018.
Inhaltsverzeichnis
Part I. Basics: 1. Introduction; 2. Counting relative frequencies; 3. Feature vectors; 4. Discriminative linear classifiers; 5. A perspective from information theory; 6. Hidden variables; Part II. Structures: 7. Generative sequence labelling; 8. Discriminative sequence labelling; 9. Sequence segmentation; 10. Predicting tree structures; 11. Transition-based methods for structured prediction; 12. Bayesian models; Part III. Deep Learning: 13. Neural network; 14. Representation learning; 15. Neural structured prediction; 16. Working with two texts; 17. Pre-training and transfer learning; 18. Deep latent variable models; Index.
Details
Erscheinungsjahr: 2021
Genre: Allg. & vergl. Sprachwissenschaft
Rubrik: Sprachwissenschaft
Medium: Buch
Seiten: 484
Inhalt: Gebunden
ISBN-13: 9781108420211
ISBN-10: 1108420214
Sprache: Englisch
Einband: Gebunden
Autor: Zhang, Yue
Teng, Zhiyang
Hersteller: Cambridge University Press
Maße: 249 x 192 x 27 mm
Von/Mit: Yue Zhang (u. a.)
Erscheinungsdatum: 07.01.2021
Gewicht: 1,17 kg
preigu-id: 118938149
Über den Autor
Yue Zhang is an associate professor at Westlake University. Before joining Westlake, he worked as a research associate at the University of Cambridge and then a faculty member at Singapore University of Technology and Design. His research interests lie in fundamental algorithms for NLP, syntax, semantics, information extraction, text generation, and machine translation. He serves as an action editor for TACL, and as area chairs of ACL, EMNLP, COLING, and NAACL. He gave several tutorials at ACL, EMNLP and NAACL, and won a best paper award at COLING in 2018.
Inhaltsverzeichnis
Part I. Basics: 1. Introduction; 2. Counting relative frequencies; 3. Feature vectors; 4. Discriminative linear classifiers; 5. A perspective from information theory; 6. Hidden variables; Part II. Structures: 7. Generative sequence labelling; 8. Discriminative sequence labelling; 9. Sequence segmentation; 10. Predicting tree structures; 11. Transition-based methods for structured prediction; 12. Bayesian models; Part III. Deep Learning: 13. Neural network; 14. Representation learning; 15. Neural structured prediction; 16. Working with two texts; 17. Pre-training and transfer learning; 18. Deep latent variable models; Index.
Details
Erscheinungsjahr: 2021
Genre: Allg. & vergl. Sprachwissenschaft
Rubrik: Sprachwissenschaft
Medium: Buch
Seiten: 484
Inhalt: Gebunden
ISBN-13: 9781108420211
ISBN-10: 1108420214
Sprache: Englisch
Einband: Gebunden
Autor: Zhang, Yue
Teng, Zhiyang
Hersteller: Cambridge University Press
Maße: 249 x 192 x 27 mm
Von/Mit: Yue Zhang (u. a.)
Erscheinungsdatum: 07.01.2021
Gewicht: 1,17 kg
preigu-id: 118938149
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