Representation Learning for Natural Language Processing Kartoniert (TB)

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.