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Representation Learning for Natural Language Processing

Inside this Book

If you make use of this material, you may credit the authors as follows:

Liu Zhiyuan et al., "Representation Learning for Natural Language Processing", Springer Nature, 2020, DOI: 10.1007/978-981-15-5573-2, License: http://creativecommons.org/licenses/by/4.0/

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.

Keywords

Natural Language Processing (nlp), Computational Linguistics, Artificial Intelligence, Data Mining And Knowledge Discovery, Open Access, Deep Learning, Representation Learning, Knowledge Representation, Word Representation, Document Representation, Big Data, Machine Learning, Natural Language Processing, Natural Language & Machine Translation, Computational Linguistics, Artificial Intelligence, Data Mining, Expert Systems / Knowledge-based Systems

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