Representation Learning for Natural Language Processing
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
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Representation Learning for Natural Language Processing
Representation Learning for Natural Language Processing
Zhiyuan Liu Yankai Lin Maosong Sun •
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Representation Learning for Natural Language Processing
123
Zhiyuan Liu Tsinghua University Beijing, China
Yankai Lin Pattern Recognition Center Tencent Wechat Beijing, China
Maosong Sun Department of Computer Science and Technology Tsinghua University Beijing, China
ISBN 978-981-15-5572-5 ISBN 978-981-15-5573-2 https://doi.org/10.1007/978-981-15-5573-2
(eBook)
© The Editor(s) (if applicable) and The Author(s) 2020. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
In traditional Natural Language Processing (NLP) systems, language entries such as words and phrases are taken as distinct symbols. Various classic ideas and methods, such as n-gram and bag-of-words models, were proposed and have been widely used until now in many industrial applications. All these methods take words as the minimum units for semantic representation, which are either used to further estimate the conditional probabilities of next words given previous words (e.g., n-gram) or used to represent semantic meanings of text (e.g., bag-of-words models). Even when people find it is necessary to model word meanings, they either manually build some
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