Inductive Dependency Parsing
This book provides an in-depth description of the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. This methodology is based on two essential components: dependency
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Newspeak was the official language of Oceania. For an account of its structure and etymology see Appendix.
Inductive Dependency Parsing
Text, Speech and Language Technology VOLUME 34
Series Editors Nancy Ide, Vassar College, New York Jean Véronis, Université de Provence and CNRS, France Editorial Board Harald Baayen, Max Planck Institute for Psycholinguistics, The Netherlands Kenneth W. Church, AT & T Bell Labs, New Jersey, USA Judith Klavans, Columbia University, New York, USA David T. Barnard, University of Regina, Canada Dan Tufis, Romanian Academy of Sciences, Romania Joaquim Llisterri, Universitat Autonoma de Barcelona, Spain Stig Johansson, University of Oslo, Norway Joseph Mariani, LIMSI-CNRS, France
Inductive Dependency Parsing by
Joakim Nivre Växjö University, Sweden
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN-10 ISBN-13 ISBN-10 ISBN-13
1-4020-4888-2 (HB) 978-1-4020-4888-3 (HB) 1-4020-4889-0 (e-book) 978-1-4020-4889-0 (e-book)
Published by Springer, P.O. Box 17, 3300 AA Dordrecht, The Netherlands. www.springer.com
Printed on acid-free paper
All Rights Reserved © 2006 Springer No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed in the Netherlands
To Elisabeth and Fredrik
Preface
This book is based on work carried out over a period of roughly three years with the support of a number of people and organizations that deserve my heartfelt gratitude. In the first place, I want to thank my PhD students Johan Hall and Jens Nilsson, who have been involved in the project from the start. I also want to thank all the people who are part of the research group in computer science at V¨axj¨ o University, for providing a stimulating environment to work in, and the Swedish Research Council for a grant that supported part of the work reported in this book (Vetenskapsr˚ adet, 621-2002-4207). Among the many people who have contributed, directly or indirectly, to the ideas and results presented in the book, I specifically want to mention Eckhard Bick, Sabine Buchholz, John Carroll, Atanas Chanev, Yuchang Cheng, Walter Daelemans, Ralph Debusmann, Denys Duchier, G¨ ulsen Eryi˘ git, Jason Eisner, Kilian Foth, Kadri Hacioglu, Jan Hajiˇc, Erhard Hinrichs, Tom´ aˇs Holan, Viggo Kann, Matthias Trautner Kromann, Geert-Jan Kruijff, Sandra K¨ ubler, Marco Kuhlmann, Haitao Liu, Welf L¨ owe, Svetoslav Marinov, Erwin Marsi, Yuji Matsumoto, Ryan McDonald, Pierre Nugues, Tomasz Obr¸ebski, Guy de Pauw, Aarne Ranta, Mario Scholz, Noah Smith, Antal van den Bosch, Hiroyasu Yamada, Anssi Yli-Jyr¨ a, and Daniel Zeman. I also want to thank my editor, Jolanda Voogd, for practical assistance, and the series editors, Nancy Ide and J
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