Representation Learning for Natural Language Processing

• Main Authors: Liu, Zhiyuan, Lin, Yankai, Sun, Maosong
• Format: eBook
• Language: English
• Published: Singapore Springer Nature 2020; Springer Singapore Pte. Limited; Springer
• Edition: 1
• Subjects: Artificial intelligence; Big Data; Computational and corpus linguistics; Computational Linguistics; Computer science; Computer Technology; Computing and Information Technology; Data mining; Data Mining and Knowledge Discovery; Deep Learning; Document Representation; Expert systems / knowledge-based systems; Knowledge Representation; Language and Linguistics; Language Arts; Linguistics; Machine Learning; Natural language & machine translation; Natural language and machine translation; Natural Language Processing; Natural language processing (Computer science); Natural Language Processing (NLP); Nonfiction; Open Access; Representation Learning; Word Representation
• ISBN: 9811555729; 9789811555725; 9789811555732; 9811555737
• DOI: 10.1007/978-981-15-5573-2

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and 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.