Representation Learning for Natural Language Processing
暫譯: 自然語言處理的表示學習

Liu, Zhiyuan, Lin, Yankai, Sun, Maosong

Representation Learning for Natural Language Processing
  • 出版商: Springer
  • 出版日期: 2020-09-18
  • 售價: $2,280
  • 貴賓價: 9.5 折 $2,166
  • 語言: 英文
  • 頁數: 334
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811555753
  • ISBN-13: 9789811555756

    • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

這本開放存取的書籍提供了有關表示學習理論、演算法和自然語言處理（NLP）應用的最新進展概述。全書分為三個部分。第一部分介紹了多語言條目的表示學習技術，包括單詞、短語、句子和文檔。第二部分則介紹了與NLP密切相關的對象的表示技術，包括基於實體的世界知識、基於語義的語言知識、網絡和跨模態條目。最後，第三部分提供了表示學習技術的開放資源工具，並討論了剩餘的挑戰和未來的研究方向。

所呈現的表示學習理論和演算法也可以惠及其他相關領域，如機器學習、社交網絡分析、語義網、信息檢索、數據挖掘和計算生物學。本書適合高年級本科生、研究生、博士後研究員、研究人員、講師和工業工程師，以及任何對表示學習和自然語言處理感興趣的人士。

作者簡介

Zhiyuan Liu is an Associate Professor at the Department of Computer Science and Technology at Tsinghua University, China. His research interests include representation learning, knowledge graphs and social computation, and he has published more than 80 papers in at leading conferences and in respected journals. He has received several awards/honors, including Excellent Doctoral Dissertation awards from Tsinghua University and the Chinese Association for Artificial Intelligence, and was named as one of MIT Technology Review Innovators Under 35 China (MIT TR-35 China). He has served as area chair for various conferences, including ACL, EMNLP, COLING.

Yankai Lin is a researcher at the Pattern Recognition Center, Tencent Wechat. He received his Ph.D. degree in Computer Science from Tsinghua in 2019. His research interests include representation learning, information extraction and question answering. He has published more than 10 papers at international conferences, including ACL, EMNLP, IJCAI and AAAI. He was named an Academic Rising Star of Tsinghua University and a Baidu Scholar.

Maosong Sun is a Professor at the Department of Computer Science and Technology and the Executive Vice Dean of the Institute for Artificial Intelligence, Tsinghua University. His research interests include natural language processing, machine learning, computational humanities and social sciences. He is the chief scientist of the National Key Basic Research and Development Program (973 Program) and the chief expert of various major National Social Science Fund of China projects. He has published over 100 papers at leading conferences and in respected journals. He is the Director of Tsinghua University-National University of Singapore Joint Research Center on Next Generation Search Technologies, and the editor-in-chief of the Journal of Chinese Information Processing. He received the Nationwide Distinguished Practitioner award from the State Commission for Language Affairs, People's Republic of China, in 2007, and the National Excellent Scientific and Technological Practitioner award from the China Association for Science and Technology in 2016.

作者簡介(中文翻譯)

劉志遠是中國清華大學計算機科學與技術系的副教授。他的研究興趣包括表示學習、知識圖譜和社會計算，並在頂尖會議和知名期刊上發表了超過80篇論文。他獲得了多項獎項/榮譽，包括清華大學和中國人工智慧學會的優秀博士論文獎，並被評選為MIT科技評論35歲以下創新者（MIT TR-35 China）。他曾擔任多個會議的區域主席，包括ACL、EMNLP和COLING。

林彥凱是騰訊微信模式識別中心的研究員。他於2019年在清華大學獲得計算機科學博士學位。他的研究興趣包括表示學習、信息提取和問答系統。他在國際會議上發表了超過10篇論文，包括ACL、EMNLP、IJCAI和AAAI。他被評選為清華大學的學術新星和百度學者。

孫茂松是清華大學計算機科學與技術系的教授及人工智慧研究院的執行副院長。他的研究興趣包括自然語言處理、機器學習、計算人文學和社會科學。他是國家重點基礎研究發展計劃（973計劃）的首席科學家，以及多個國家社會科學基金重大項目的首席專家。他在頂尖會議和知名期刊上發表了超過100篇論文。他是清華大學與新加坡國立大學下一代搜索技術聯合研究中心的主任，以及《中文信息處理》期刊的主編。他於2007年獲得中華人民共和國國家語言文字工作委員會頒發的全國優秀實踐者獎，並於2016年獲得中國科學技術協會頒發的全國優秀科技工作者獎。

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