Conceptual Information Retrieval: A Case Study in Adaptive Partial Parsing

229 Pages·1991·16.528 MB·English

by  Michael L. Mauldin (auth.)

CONCEPTUALINFO~TION
RETRIEVAL
A Case Study in Adaptive
Partial Parsing
THEKLUWER INTERNATIONALSERIES IN
ENGINEERINGAND COMPUTERSCIENCE
NATURALLANGUAGE PROCESSING
AND MACIllNETRANSLATION
ConsultingEditor
JaimeCarbonell
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CONCEPTUALINFORMATION
RETRIEVAL
A ease Study in Adaptive
Partial Parsing
by
Michael L Mauldin
Carnegie Mellon University
with a foreword by
Jaime G. Carbonell
~.
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SPRINGER SCIENCE+BUSINESS MEDIA, LLC
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Library Congress Cataloging.in.Publication Data
Mauldin, Michael L., 1959-
Conceptual information retrieval : a case study in adaptive
partial parsing / by Michael L. Mauldin.
p. cm. --(The Kluwer international series in engineering and
computer science; interna!. vo!. # 152. Naturallanguage processing
and machine translation)
Includes bibliographical references and index.
ISBN 978-1-4613-6790-1 ISBN 978-1-4615-4004-5 (eBook)
DOI 10.1007/978-1-4615-4004-5
1. Natural language processing (Computer science) 2. FERRET
(Information retrieval system) 3. Computationallinguistics.
I. Title. II. Series: Kluwer international series in engineering
and computer science; SECS 152. 1II. Series: Kluwer international
series in engineering and computer science. Natural language
processing and machine translation.
QA76.9.N38M38 1991
006.3'5--dc20 91-27108
CIP
Copyright © 1991 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1991
Softcover reprint ofthe hardcover 1st edition 1991
All 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 wrilten permission of the publisher, Springer Science+Business
Media,LLC.
Printed on acid-free paper.
To my wife Michelle
TABLE OF CONTENTS
LIST OF FIGURES xi
FOREWORD xv
Jaime Carbonell
PREFACE xvii
ACKNOWLEDGMENTS xix
1. INTRODUCTION 1
1.1. Motivation 1
1.2. The Keyword Barrier 2
1.3. Beyond the Keyword Barrier: Conceptual Matching 6
1.4. Definitions 8
1.5. Perfonnance Measures for Infonnation Retrieval 11
1.6. Goals ofthe FERRETproject 12
1.7. Learning 14
1.8. ChapterOverview 14
2. CURRENT APPROACHES 17
2.1. Postcoordinate Systems 18
2.2. Precoordinate Systems 24
2.3. Knowledge Rich Representations for Retrieval 27
2.4. Problems with the State ofthe Art 35
2.5. What Kinds ofLearning are Possible 36
VIU CONCEPTUALINFORMAnONRETRIEVAL
3. CONCEPTUAL UNDERSTANDING OF TEXT 39
3.1. NaturalLanguageParsing 39
3.2. CaseFramesforUnderstanding 42
3.3. FRUMP 46
4. THE FERRET SYSTEM SS
4.1. Architectural Overview 55
4.2. Scanner 69
4.3. TheMcFRUMPParser 72
4.4. DictionaryInterface 82
4.5. CaseFrame Matching 83
4.6. UserInterface 86
4.7. Summary 86
S. SCRIPT LEARNING 89
5.1. Approaches to Machine Learning 89
5.2. Tools for Learning: Genetic Algorithms 91
5.3. Design ofLearning Component 95
5.4. ExampleofNew Script Being Learned 102
5.5. Caveat 106
5.6. Summary 106
6. EMPIRICAL STUDIES 109
6.1. Domains for Studying Retrieval 109
6.2. ParsingEffectiveness 115
6.3. EffectofUsing Dictionary 116
6.4. FERRETversus Boolean Keyword Query 118
6.5. Possible Sources ofError 130
7. CONCLUSION 133
7.1. Objectives Met 133
7.2. Applications 135
7.3. FutureWork 141
7.4. Conclusion 151
TABLEOFCONTENTS ix
Appendix A. SAMPLE DATA STRUCTURES 153
A.l. SampleTexts 153
A.2. SampleTexts afterScanning 158
A.3. SampleLexicalEntries 161
A.4. Webster's Seventh Dictionary 163
A.5. Sample Sketchy Scripts 167
A.6. Sample Texts afterParsing 168
A.7. Sample Retrieval 169
Appendix B. USER QUERIES USED IN THE 171
STUDIES
B.l. Sample User Survey Form 171
B.2. UserQueries Usedin the Study 173
Appendix C. RAW DATA FOR EMPIRICAL 177
STUDIES
Appendix D. THE NUMBER AND DATE 181
GRAMMARS
REFERENCES 193
INDEX 211
LIST OF FIGURES
Figure 1-1: Synonymy and Polysemy 3
Figure 1-2: The Keyword Barrier 5
Figure 1-3: Canonicality ofCD representation improves 7
recall
Figure 2-1: An Example ofInverted Files 20
Figure 2-2: MajorDivisionsofthe Dewey Decimal System 25
Figure 2-3: AND/OR tree for text classification 30
Figure 3-1: Sample Input Story for FRUMP 47
Figure 3-2: FRUMP'Soutput 48
Figure 3-3: Portion ofan SSIDT 52
Figure 4-1: SimpleDiagram ofFERRET 56
Figure 4-2: Relevance Homomorphism Assumption 57
Figure 4-3: ASample STARDATEText 59
Figure 4-4: AnnotatedTraceofa Sample ParserRun 60
Figure 4-5: Sample Retrieval by Script 65
Figure 4-6: Sample Retrieval by CD graph 67
Figure 4-7: Requesting an Abstract 68
Figure 4-8: Sample Scanned Text 70
Figure 4-9: Sample ScannerOutput 72
Figure 4-10: The MCFRUMPParser 74
Figure 4-11: Types ofLexical Information in McFRuMP 77
Figure 4-12: Part ofthe MCFRUMP IS-A Hierarchy 77
Figure 4-13: The Original FRUMPVehicle AccidentScript 80
xii CONCEPTUALINFORMAnONRETRIEVAL
Figure 4-14: The McFRUMP Vehicle Accident Script 81
Figure 4-15: APortion ofFERRET'sSSmT 81
Figure 4-16: Sample Inputand OutputofDictionary 84
Lookup
Figure 4-17: FERRETwithoutLearning 87
Figure 5-1: R •A Single ReplacementGenetic Algorithm 93
I
Figure 5-2: Crossoveras Sexual Reproduction 94
Figure 5-3: Learning Component Architecture 95
Figure 5-4: New ScriptHypothesis Algorithm 99
Figure 5-5: Hypothetical ExampleofScript Specialization 100
Figure 5-6: Hypothetical Example ofScript Crossover 100
Figure 5-7: The Original $LAUNCH Script 103
Figure 5-8: Spacecraft vs Telescope in the IS-A Hierarchy 103
Figure 5-9: The Learned $LAUNCH Script 104
Figure 5-10: Retrieval using newly learned Launch script 105
Figure 5-11: FERRETplus Learning 107
Figure 6-1: Sample Dow Jones News Story 110
Figure 6-2: FERRET's definitions for''rise" 111
Figure 6-3: Parsing the Sample Dow Jones Story 112
Figure 6-4: Astronomy story using "rise" 113
Figure 6-5: Parsing the Astronomy Story 113
Figure 6-6: ParsingEffectiveness 116
Figure 6-7: EffectofDictionary with time limits 117
Figure 6-8: EffectofDictionary without time limits 118
Figure 6-9: Faking a query parser with McFRUMP 121
Figure 6-10: Ferretversus Keywords -Training Set 122
Figure 6-11: Retrieval Performance without Learning - 122
Training Set
Figure 6-12: Ferretversus Keywords -Evaluation Set 123
Figure 6-13: Retrieval Performance without Learning - 123
Evaluation Set
Figure 6-14: EffectofScriptLearning -Training Set 126
Figure 6-15: Retrieval Performance with Learning 126
Figure 6-16: EffectofScript Learning -Evaluation Set 127