<DOCINFO AUTHOR "" TITLE "Analogical Modeling: An exemplar-based approach to language" SUBJECT "HCP, Volume 10" KEYWORDS "" SIZE HEIGHT "220" WIDTH "150" VOFFSET "4"> Analogical Modeling human cognitive processing is a forum for interdisciplinary research on the nature and organization of the cognitive systems and processes involved in speaking and understanding natural language (including sign language), and their relationship to other domains of human cognition, including general conceptual or knowledge systems and processes (the language and thought issue), and other perceptual or behavioral systems such as vision and non- verbal behavior (e.g. gesture). ‘Cognition’ should be taken broadly, not only including the domain of rationality, but also dimensions such as emotion and the unconscious. The series is open to any type of approach to the above questions (methodologically and theoretically) and to research from any discipline, including (but not restricted to) different branches of psychology, artificial intelligence and computer science, cognitive anthropology, linguistics, philosophy and neuroscience. It takes a special interest in research crossing the boundaries of these disciplines. Editors Marcelo Dascal, Tel Aviv University Raymond W. Gibbs, University of California at Santa Cruz Jan Nuyts, University of Antwerp Editorial address Jan Nuyts, University of Antwerp, Dept. of Linguistics (GER), Universiteitsplein 1, B 2610 Wilrijk, Belgium. E-mail: [email protected] Editorial Advisory Board Melissa Bowerman, Nijmegen; Wallace Chafe, Santa Barbara, CA; Philip R. Cohen, Portland, OR; Antonio Damasio, Iowa City, IA; Morton Ann Gernsbacher, Madison, WI; David McNeill, Chicago, IL; Eric Pederson, Eugene, OR; François Recanati, Paris; Sally Rice, Edmonton, Alberta; Benny Shanon, Jerusalem; Lokendra Shastri, Berkeley, CA; Dan Slobin, Berkeley, CA; Paul Thagard, Waterloo, Ontario Volume 10 Analogical Modeling: An exemplar-based approach to language Edited by Royal Skousen, Deryle Lonsdale and Dilworth B. Parkinson Analogical Modeling An exemplar-based approach to language Edited by Royal Skousen Deryle Lonsdale Dilworth B. Parkinson Brigham Young University, Provo, Utah John Benjamins Publishing Company Amsterdam/Philadelphia TM The paper used in this publication meets the minimum requirements of American 8 National Standard for Information Sciences – Permanence of Paper for Printed Library Materials, ansi z39.48-1984. Library of Congress Cataloging-in-Publication Data Analogical Modeling : An exemplar-based approach to language / edited by Royal Skousen, Deryle Lonsdale and Dilworth B.Parkinson. p.cm.(Human Cognitive Processing, issn 1387–6724 ; v.10) “Much of the Language, held at Brigham Young University on 22-24 March 2000” p.8. Includes bibliographical references and indexes. 1.Analogy (Linguistics) 2.Psycholinguistics.I.Skousen, Royal.II.Lonsdale, Deryle. III.Parkingson, Dilworth B.,1951- IV.Conference on Analogical Modeling of the Language (2000: Brigham Young University) V.Series. P299.A48 A53 2002 417´.7-dc21 2002028283 isbn 9027223629(Eur.)/1588113027 (US) (Hb; alk.paper) © 2002 – John Benjamins B.V. No part of this book may be reproduced in any form, by print, photoprint, microfilm, or any other means, without written permission from the publisher. John Benjamins Publishing Co.· P.O.Box 36224 · 1020 me Amsterdam · The Netherlands John Benjamins North America · P.O. Box 27519 · Philadelphia pa 19118-0519 · usa Table of contents List of contributors ix Introduction 1 Royal Skousen I. The basics of Analogical Modeling Chapter 1 An overview of Analogical Modeling 11 Royal Skousen Chapter 2 Issues in Analogical Modeling 27 Royal Skousen II. Psycholinguistic evidence for Analogical Modeling Chapter 3 Skousen’s analogical approach as an exemplar-based model of categorization 51 Steve Chandler III. Applications to specific languages Chapter 4 Applying Analogical Modeling to the German plural 109 Douglas J. Wulf Chapter 5 Testing Analogical Modeling: The /k/∼Ø alternation in Turkish 123 C. Anton Rytting Table of contents IV. Comparing Analogical Modeling with TiMBL Chapter 6 A comparison of two analogical models: Tilburg Memory-Based Learner versus Analogical Modeling 141 David Eddington Chapter 7 A comparison of Analogical Modeling to Memory-Based Language Processing 157 Walter Daelemans Chapter 8 Analogical hierarchy: Exemplar-based modeling of linkers in Dutch noun-noun compounds 181 Andrea Krott, Robert Schreuder, and R. Harald Baayen V. Extending Analogical Modeling Chapter 9 Expanding k-NN analogy with instance families 209 AntalvandenBosch Chapter 10 Version spaces, neural networks, and Analogical Modeling 225 Mike Mudrow Chapter 11 Exemplar-driven analogy in Optimality Theory 265 James Myers Chapter 12 The hope for analogous categories 301 Christer Johansson Table of contents VI. Quantum computing and the exponential explosion Chapter 13 Analogical Modeling and quantum computing 319 Royal Skousen VII. Appendix Chapter 14 Data files for Analogical Modeling 349 Deryle Lonsdale Chapter 15 Running the Perl/C version of the Analogical Modeling program 365 Dilworth B. Parkinson Chapter 16 Implementing the Analogical Modeling algorithm 385 Theron Stanford Index 411 List of contributors R. Harald Baayen Deryle Lonsdale [email protected] [email protected] Associate Professor of Department of Linguistics General Linguistics and English Language University of Nijmegen Brigham Young University The Netherlands Provo, Utah, USA Steve Chandler Mike Mudrow [email protected] [email protected] Associate Professor of English Department of Languages University of Idaho and Philosophy Moscow, Idaho, USA Utah State University Logan, Utah, USA Walter Daelemans [email protected] James Myers Professor of [email protected] Computational Linguistics Graduate Institute of Linguistics University of Antwerp, Belgium National Chung Cheng University Taiwan, Republic of China David Eddington [email protected] Dilworth B. Parkinson Associate Professor of [email protected] Spanish Linguistics Professor of Arabic University of New Mexico Brigham Young University Albuquerque, New Mexico, USA Provo, Utah, USA Christer Johansson C. Anton Rytting [email protected] [email protected] Associate Professor of Department of Linguistics Computational Linguistics Ohio State University University of Bergen, Norway Columbus, Ohio, USA Andrea Krott Robert Schreuder [email protected] [email protected] Department of Linguistics Professor of Psycholinguistics University of Alberta University of Nijmegen Edmonton, Alberta, Canada The Netherlands List of contributors Royal Skousen Antal van den Bosch [email protected] [email protected] Professor of Linguistics Faculty of Arts and English Language Tilburg University Brigham Young University The Netherlands Provo, Utah, USA Douglas J. Wulf Theron Stanford [email protected] [email protected] Department of Linguistics Department of Asian and University of Washington Near Eastern Languages Seattle, Washington, USA Brigham Young University Provo, Utah, USA Introduction Royal Skousen Competing models of language description Much of the debate in language description involves choosing between alterna- tive methods of describing language. A number of completely different systems for predicting language behavior have been developed in the past two decades. The original, traditional approach (actually centuries old) has been to devise specific rules. In such a declarative approach, rules are used to divide up the contex- tual space into distinct conditions and then to specify the corresponding behavior for each of those conditions. Such a traditional approach seems unavoidable for telling someone else how something behaves. Although rules may not actually be used by speakers in producing their own language, we still use rules to tell others how language works (even when describing the results of analogical modeling). In general, rule approaches form the basis for most descriptive systems, such as expert systems for doing medical diagnoses. The idea behind such medical systems is to specify for a given set of symptoms a corresponding disease or dysfunction. In contrast to declarative, rule-based approaches, there are two different kinds of procedural, non-rule systems that have been developed in the last part of the twentieth century: neural networks and exemplar-based systems. These procedu- ral approaches have no explicit statement of regularities. They use examples to set up a system of connections between possible predictive variables or they directly use the examples themselves to predict behavior. A procedural approach typically predicts behavior for only a given item. Generally speaking, directly accessible in- formation about what variables lead to the prediction is not available – thus, the non-declarative nature of these approaches. Neural networks had their beginnings in the middle of the twentieth century, but were then ignored for several decades until researchers realized that output nodes did not have to be directly predicted from input nodes. Hidden levels of nodes could be used to represent various levels of activation and thus learn vir- tually any kind of relationship between variables, providing certain possibilities Royal Skousen (such as back propagation) were allowed between levels of nodes. Although many of the kinds of connections between nodes seem impossible from a neurological point of view, the “neurological temptation” has been strong since the 1980s