Adaptive Parsing: Self-Extending Natural Language Interfaces

249 Pages·1992·14.619 MB·

by  Jill Fain Lehman (auth.)

ADAPTIVE PARSING
Self-Extending Natural Language
Interfaces
THE KLUWER INTERNATIONAL SERIES IN
ENGINEERING AND COMPUTER SCIENCE
NATURAL LANGUAGE PROCESSING
AND MACHINE TRANSLATION
Consulting Editor
Jaime CarboneU
Other books in the series:
EFFICIENT PARSING FOR NATURAL LANGUAGE: A FAST ALGORITHM FOR
PRACTICAL SYSTEMS, M. Tomita
ISBN 0-89838-202-5
A NATURAL LANGUAGE INTERFACE FOR COMPUTER AIDED DESIGN, T. Samad
ISBN 0-89838-222-X
INTEGRATED NATURAL LANGUAGE DIALOGUE: A COMPUTATIONAL MODEL,
R.E. Frederking
ISBN 0-89838-255-6
NAIVE SEMANTICS FOR NATURAL LANGUAGE UNDERSTANDING, K. Dahlgren
ISBN 0-89838-287-4
UNDERSTANDING EDITORIAL TEXT: A Computer Model of Argument Comprehension,
S.l. Alvarado
ISBN: 0-7923-9123-3
NATURAL LANGUAGE GENERATION IN ARTIFICIAL INTElliGENCE AND
COMPUTATIONAL UNGUISTICS, ParislSwartout/Mann
ISBN: 0-7923-9098-9
CURRENT ISSUES IN PARSING TECHNOLOGY, M. Tomita
ISBN: 0-7923-9131-4
CONCEPTUAL INFORMATION RETRIEVAL: A Case Study in Adaptive Partial Parsing,
M.I-Mauldin
ISBN: 0-7923-9214-0
GENERAUZED I-R. PARSING, M. Tomita
ISBN: 0-7923-9201-9
ADAPTIVE PARSING
Self-Extending Natural Language
Interfaces
by
Jill Fain Lehman
Carnegie Mellon University
.
.,
~
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
Library ofCongress Cataloging-in-Publication Data
Lehman, Jill Fain.
Adaptive parsing : self-extending naturallanguage interfaces / by
Jill Fain Lehman.
p. cm. -- (Kluwer international series in engineering and
computer science. Naturallanguage processing and machine
translation)
Includes bibliographical references and index.
ISBN 978-1-4613-6610-2 ISBN 978-1-4615-3622-2 (eBook)
DOI 10.1007/978-1-4615-3622-2
1. Naturallanguage processing (Computer Science) 2. Human
-computer interaction. 1. Title. II. Series. III. Series: Kluwer
international series in engineering and computer science.
QA76.9.N38L44 1992
006.3'5--dc20 91-35582
CIP
Copyright © 1992 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1992
Softcover reprint ofthe hardcover Ist edition 1992
AII rights reserved. No part of this publication may be reproduced, stored in a retrieval
system or transmitted in any form or by any means, mechanical, photo-copying, recording,
or otherwise, without the prior written permission of the publisher, Springer Science
+Business Media, LLC
Printed 011 acid-free paper.
To Mom and Dad and Ken
for always making me get the dictionary
"I don't know what you mean by 'glory,' " Alice said.
Humpty Dumpty smiled contemptuously. "Of course you
don't-till I tell you. I meant't here's a nice knock-down argument
for you!' "
"But 'glory' doesn't mean 'a nice knock-down argument,' "
Alice objected.
"When I use a word," Humpty Dumpty said, in rather a scorn
ful tone, "it means just what I choose it to mean-neither more
nor less."
"The question is," said Alice, "whether you can make words
mean so many different things."
"The question is," said Humpty Dumpty, "which is to be
master-that's all."
- Lewis Carroll
Alice's Adventures in Wonderland
Table of Contents
1. Introduction 1
1.1. Natural language interfaces 1
1.2. The dilemma 2
1.3. Towards a solution 4
1.4. The frequent user 6
1.5. Conclusions 7
1.6. guide 7
Reader'~
2. System Behavior in an Adaptive Environment 11
2.1. Foundations: Least-deviant-first parsing and MULTIPAR 11
2.2. The model 13
2.3. Assumptions and hypotheses 18
2.4. Adaptation vs. customizable and instructable interfaces 20
2.5. Prior research in adaptation 22
3. User Behavior in an Adaptive Environment 29
3.1. The behavioral hypotheses 30
3.2. The experimental condition 31
3.3. Control conditions 34
3.4. User profiles 35
3.5. Results and discussion 36
4. System Architecture and Knowledge Representation 45
4.1. Representing knowledge in CHAMP 47
4.2. Learning-related knowledge 49
4.3. Application-related knowledge 61
5. Understanding Non-Deviant Utterances 67
5.1. Segmentation 69
5.2. The CoalescelExpand Cycle 81
5.3. A detailed example: the Coalesce/Expand Cycle for "Can- 89
eel the 3 p.m. speech research meeting on June 16."
6. Detecting and Recovering from Deviation 95
6.1. Parsing as least-deviant-first search 97
6.2. The cache 104
6.3. Error detection 107
6.4. A detailed example: Error detection during the parse of 109
"Schedule a meeting at 3 pm June 7."
viii
6.5. Error recovery 115
7. Resolution: Choosing Among Explanations 129
7.1. Converting meaning to action 133
7.2. Using default knowledge and inference 137
7.3. Interacting with the databases 142
7.4. Confirmation by etTect 147
8. Adaptation and Generalization 149
8.1. Adapting to substitution deviations 152
8.2. Adapting to insertion deviations 158
8.3. Adapting to deletion deviations 160
8.4. Adapting to transposition deviations 162
8.5. A summary of adaptation in CHAMP 165
8.6. Controlling growth in the grammar through competition 170
8.7. The etTects of adaptation on performance 176
9. Evaluating the Interface 181
9.1. CHAMP's performance on the hidden-operator data 182
9.2. CHAMP's performance in real user interactions 203
9.3. Summary of results 211
10. Critical Issues 213
10.1. The etTect of kernel design on learning 213
10.2. The etTect of kernel design on search 216
10.3. The etTect of search constraints on system predictability 217
10.4. The lexical extension problem 219
10.5. Summary 222
11. Conclusions and Future Directions 223
11.1. Main results 223
11.2. Future directions 225
References 231
Index 237
Foreword
As the computer gradually automates human-oriented tasks in multiple environ
ments, the interface between computers and the ever-wider population of human
users assumes progressively increasing importance. In the office environment, for
instance, clerical tasks such as document filing and retrieval, and higher-level tasks
such as scheduling meetings, planning trip itineraries, and producing documents for
publication, are being partially or totally automated. The range of users for office
oriented software includes clerks, secretaries, and businesspersons, none of whom
are predominantly computer literate. The same phenomenon is echoed in the factory
production line, in the securities trading floor, in government agencies, in educa
tional institutions, and even in the home. The arcane command languages of yes
teryear have proven too high a barrier for smooth acceptance of computerized func
tions into the workplace, no matter how useful these functions may be. Computer
naive users simply do not take the time to learn intimidating and complex computer
interfaces.
In order to place the functionality of modem computers at the disposition of
diverse user populations, a number of different approaches have been tried, many
meeting with a significant measure of success, to wit: special courses to train users
in the simpler command languages (such as MS-DOS), designing point-and-click
menu/graphics interfaces that require much less user familiarization (illustrated most
clearly in the Apple Macintosh), and interacting with the user in his or her language
of choice. The latter category includes spreadsheet programs for interacting with
accountants, markup languages for interacting with editors and writers, and natural
languages for interacting with just about everyone else in a wide variety of tasks
ranging from data base query to expert system command interfaces. By "natural
language," of course, we mean English, French, German, Spanish, Japanese, etc. or,
rather, the subset of the selected language needed for the range of tasks at hand. The
central philosophy is to build the interface so as to exploit existing human means of
communication-to adapt the computer to human abilities, rather than vice-versa.
Natural language interfaces are not yet in widespread use for a variety of reasons,
ranging from the sheer complexity of the natural language comprehension task to
x
ergonomic limitations such as poor typing skills in some user populations and lack of
robust error-recovery methods. The technology, however, moves inexorably for
ward: newer and faster language comprehension methods are under development.
The accuracy and speed of speech-recognition systems is approaching acceptable
levels, hence permitting the substitution of a microphone for the keyboard. And the
computing power available on the desktop is approaching the levels required for
real-time language comprehension. Is it then only a matter of time before natural
language becomes a ubiquitous interface technology? A few scientific challenges
remain, such as semi-automated customization of the linguistic knowledge to the
task-domain and to the idiosyncracies of the individual user.
Jill Fain Lehman addresses the second of these challenges in this monograph: how
to build a self-adapting natural language interface that learns the modes of expres
sion of each particular user, thereby improving interpretation accuracy over time.
Heretofore, natural language interfaces suffered from inherent fragility because their
coverage (grammar and vocabulary) were necessarily limited, and any user utterance
outside this self-imposed boundary would cause an interpretation error. The better
systems provide some diagnosis of the error permitting the user to rephrase and
hopefully complete the intended communication. Would it not be much better if the
interface learned the new word or grammatical construct, so next time it would
understand it correctly without necessitating a clarification subdialog? Such is the
motivation for the work described in this book: habitability means never having to
say you're sorry, twice. In other words a major component in making a natural
language interface habitable, beyond those enumerated by Harry Tennant [69] is the
ability to accommodate necessarily unpredictable idiosyncratic usage of language,
and to do so systematically and reliably.
If natural language interfaces require extensive coverage, why not build an all
encompassing grammar and dictionary to cover all possible normal, ideolectic, and
idiosyncratic usage of language in all domains? Setting aside the sheer enormity of
such a task, there are inherent problems in that similar or even identical expressions
used in different domains by different users can have different meanings. In other
words, as the number of possible interpretations of natural language utterances grows
so does the language comprehension time and the ambiguity of the resultant inter
pretations. The problem of occasionally not understanding an idiosyncratic expres
sion would be replaced by the problem of repeatedly having multiple spurious inter
pretations of the same utterances. Such a tradeoff is not necessarily beneficial.
Instead, Lehman chooses to start with a kernel grammar and dictionary and augment
xi
it incrementally on an if-needed basis when interpretation impasses are reached and
resolved. Moreover, she chooses to do so separately for each frequent user of the
interface. Lehman's philosophy is adapting the computer interface to the needs of
human users carried to its ultimate extreme: adaptation to each and every frequent
user.
In order for automated adaptation of a natural language interface to be effective,
certain assumptions must be verified, such as cross-user linguistic variability and
within-user linguistic constancy. In other words, if there is no idiosyncratic linguis
tic usage, adaptation is not required, and if each user changes her language daily
adaptation is not possible. Lehman verifies both assumptions with longitudinal
hidden-operator experiments, and goes on to design, build, and evaluate a natural
language interface that adapts to the linguistic preferences of each individual user,
demonstrating significant improvements in comprehension accuracy. To determine
how this adaptation technology is developed and applied, of course, requires reading
the book. The ability to adapt to individual frequent users is one more key com
ponent towards the development of habitable natural language interfaces with wide
spread user acceptance.
-Jaime Carbonell