Semi-Supervised Dependency Parsing
This book presents a comprehensive overview of semi-supervised approaches to dependency parsing. Having become increasingly popular in recent years, one of the main reasons for their success is that they can make use of large unlabeled data together with
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miSupervised Dependency Parsing
Semi-Supervised Dependency Parsing
Wenliang Chen • Min Zhang
Semi-Supervised Dependency Parsing
123
Min Zhang Soochow University Suzhou, Jiangsu, China
Wenliang Chen Soochow University Suzhou, Jiangsu, China
ISBN 978-981-287-551-8 DOI 10.1007/978-981-287-552-5
ISBN 978-981-287-552-5 (eBook)
Library of Congress Control Number: 2015941148 Springer Singapore Heidelberg New York Dordrecht London © Springer Science+Business Media Singapore 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. 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. Printed on acid-free paper Springer Science+Business Media Singapore Pte Ltd. is part of Springer Science+Business Media (www. springer.com)
Preface
Semi-supervised approaches for dependency parsing have become increasingly popular in recent years. One of the reasons for their success is that they can make use of large unlabeled data together with relatively small labeled data and have shown their advantages on the task of dependency parsing for many languages. A range of different semi-supervised dependency parsing approaches have been proposed in recent work which utilize different types of information learned from unlabeled data. The aim of this book is to give readers a comprehensive introduction to the semi-supervised approaches for dependency parsing. This book is targeted to be a textbook for advanced undergraduate and graduate students and researchers in syntactic parsing and natural language processing. This book is partly derived from our earlier publications. We want to thank our coauthors in those publications: Hitoshi Isahara, Daisuke Kawahara, Jun’ichi Kazama, Kentaro Torisawa, Yoshimasa Tsuruoka, Kiyotaka Uchimoto, Yiou Wang, Yujie Zhang, Xiangyu Duan, Zhenghua Li, Haizhou Li, and Yue Zhang. We also want to thank the attendees in the IJCNLP2013 and COLING2014 tutorials on Dependency Parsing: Past, Present, and Future, presented by Zhenghua Li, Wenliang Chen, and Min Zhang. This book is also partly based on the material from the tutor
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