Название: Representation Learning for Natural Language Processing, Second Edition
Автор: Zhiyuan Liu, Yankai Lin, Maosong Sun
Издательство: Springer
Год: 2023
Страниц: 535
Язык: английский
Формат: pdf (true)
Размер: 14.7 MB
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. This book is designed for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, industrial engineers, and anyone interested in representation learning, NLP, knowledge engineering, and AI. This is not a textbook, and we don’t introduce basic knowledge. We expect the readers to have prior knowledge of Probability, Linear Algebra, and Machine Learning.
