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Robert Berwick - The Acquisition of Syntactic Knowledge (Artificial Intelligence)

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This landmark work in computational linguistics is of great importance both theoretically and practically because it shows that much of English grammar can be learned by a simple program.The Acquisition of Syntactic Knowledge investigates the central questions of human and machine cognition: How do people learn language? How can we get a machine to learn language? It first presents an explicit computational model of language acquisition which can actually learn rules of English syntax given a sequence of grammatical, but otherwise unprepared, sentences.It shows that natural languages are designed to be easily learned and easily processed-an exciting breakthrough from the point of view of artificial intelligence and the design of expert systems because it shows how extensive knowledge might be acquired automatically, without outside intervention. Computationally, the book demonstrates how constraints that may be reasonably assumed to aid sentence processing also aid language acquisition.Chapters in the books second part apply computational methods to the general problem of developmental growth, particularly the thorny problem of the interaction between innate genetic endowment and environmental input, with the intent of uncovering the constraints on the acquisition of syntactic knowledge.A number of mini-theories of learning are incorporated in this study of syntax with results that should appeal to a wide range of scholarly interests. These include how lexical categories, phonological rule systems, and phrase structure rules are learned; the role of semantic-syntactic interaction in language acquisition; how a parameter setting model may be formalized as a learning procedure; how multiple constraints (from syntax, thematic knowledge, or phrase structure) interact to aid acquisition; how transformational-type rules may be learned; and, the role of lexical ambiguity in language acquisition.Robert Berwick is an assistant professor in the Department of Electrical Engineering and Computer Science at MIT. The Acquisition of Syntactic Knowledge is sixteenth in the Artificial Intelligence Series, edited by Patrick Winston and Michael Brady.

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title The Acquisition of Syntactic Knowledge MIT Press Series in - photo 1

title:The Acquisition of Syntactic Knowledge MIT Press Series in Artificial Intelligence
author:Berwick, Robert C.
publisher:MIT Press
isbn10 | asin:0262022265
print isbn13:9780262022262
ebook isbn13:9780585368917
language:English
subjectArtificial intelligence, Computational linguistics, Language acquisition, Learning--Mathematical models.
publication date:1985
lcc:Q335.B48 1985eb
ddc:401.9
subject:Artificial intelligence, Computational linguistics, Language acquisition, Learning--Mathematical models.
Page i
The Acquisition of Syntactic Knowledge
Page ii
The MIT Press Series in Artificial Intelligence
Edited by Patrick Henry Winston and Michael Brady
Artificial Intelligence: An MIT Perspective, Volume I: Expert Problem Solving, Natural Language Understanding, Intelligent Computer Coaches, Representation and Learning edited by Patrick Henry Winston and Richard Henry Brown, 1979
Artificial Intelligence: An MIT Perspective, Volume II: Understanding Vision, Manipulation, Computer Design, Symbol Manipulation edited by Patrick Henry Winston and Richard Henry Brown, 1979
NETL: A System for Representing and Using Real-World Knowledge by Scott Fahlman, 1979
The Interpretation of Visual Motion by Shimon Ullman, 1979
A Theory of Syntactic Recognition for Natural Language by Mitchell P. Marcus, 1980
Turtle Geometry: The Computer as a Medium for Exploring Mathematics by Harold Abelson and Andrea diSessa, 1981
From Images to Surfaces: A Computational Study of the Human Early Visual System by William Eric Leifur Grimson, 1981
Robot Manipulators: Mathematics, Programming and Control by Richard P. Paul, 1981
Computational Models of Discourse edited by Michael Brady and Robert C. Berwick, 1982
Robot Motion: Planning and Control by Michael Brady, John M. Hollerbach, Timothy Johnson, Tomas Lozano-Prez, and Matthew Mason, 1982
In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension by Michael G. Dyer, 1983
Robotics Research: The First International Symposium edited by Michael Brady and Richard Paul, 1984
Robotics Research: The Second International Symposium edited by Hideo Hanafusa and Hirochika Inoue, 1985
Robot Hands and the Mechanics of Manipulation by Matthew T. Mason and J. Kenneth Salisbury, Jr., 1985
Legged Robots that Balance by Marc Raibert, 1985
The Acquisition of Syntactic Knowledge by Robert C. Berwick, 1985
Page iii
The Acquisition of Syntactic Knowledge
Robert C. Berwick
Page iv PUBLISHERS NOTE This format is intended to reduce the cost of - photo 2
Page iv
PUBLISHER'S NOTE
This format is intended to reduce the cost of publishing certain works in book form and to shorten the gap between editorial preparation and final publication. The time and expense of detailed editing and composition in print have been avoided by photographing the text of this book directly from the author's typeset copy.
Second printing, 1986
Copyright 1985 by The Massachusetts Institute of Technology.
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form, or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.
This book was typeset with Donald E. Knuth's Picture 3 and Leslie Lamport'sPicture 4.
Printed and bound in the United States of America.
Library of Congress Cataloging in Publication Data
Berwick, Robert C.
The acquisition of syntactic knowledge.
(The MIT Press series in artificial intelligence)
Bibliography: p.
Includes index.
1. Artificial intelligence. 2. LinguisticsData
processing. 3. Language acquisition. 4. Learning
Mathematical models. I. Title. II. Series.
Q335.B48 1985 401.9 85 11460
ISBN 0 262 02226 5
Page v
CONTENTS
Series Foreword
vii
Preface
ix
Part I
The Computer Model
Chapter 1
Computation and Language Acquisition
3
Picture 5
The Acquisition Model is Simple
10
Picture 6
Constraints Make Learning Possible
24
Picture 7
Evaluating the Model
38
Picture 8
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