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Natural language information retrieval

Natural language information retrieval (3회 대출)

자료유형
단행본
개인저자
Strzalkowski, Tomek.
서명 / 저자사항
Natural language information retrieval / edited by Tomek Strzalkowski.
발행사항
Dordrecht ;   Boston :   Kluwer Academic,   c1999.  
형태사항
xxv, 384 p. ; 25 cm.
총서사항
Text, speech, and language technology ; v. 7
ISBN
0792356853 (acid free paper)
서지주기
Includes bibliographical references and index.
일반주제명
Computational linguistics. Information storage and retrieval systems. Computational linguistics. Information storage and retrieval systems.
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008 990223s1999 ne b 001 0 eng
010 ▼a 99022755
020 ▼a 0792356853 (acid free paper)
040 ▼a DLC ▼c DLC ▼d UKM ▼d C#P ▼d OHX
042 ▼a pcc
049 ▼l 111141665
050 0 0 ▼a P98 ▼b .N297 1999
072 7 ▼a P ▼2 lcco
082 0 0 ▼a 410/.285 ▼2 21
090 ▼a 410.285 ▼b N285
245 0 0 ▼a Natural language information retrieval / ▼c edited by Tomek Strzalkowski.
260 ▼a Dordrecht ; ▼a Boston : ▼b Kluwer Academic, ▼c c1999.
300 ▼a xxv, 384 p. ; ▼c 25 cm.
440 0 ▼a Text, speech, and language technology ; ▼v v. 7
504 ▼a Includes bibliographical references and index.
650 0 ▼a Computational linguistics.
650 0 ▼a Information storage and retrieval systems.
650 4 ▼a Computational linguistics.
650 4 ▼a Information storage and retrieval systems.
700 1 ▼a Strzalkowski, Tomek.
938 ▼a Otto Harrassowitz ▼b HARR ▼n har005089746 ▼c 213.00 DEM

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/서고6층/ 청구기호 410.285 N285 등록번호 111141665 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차


CONTENTS

PREFACE = xiii

CONTRIBUTING AUTHORS = xxiii

1 WHAT IS THE ROLE OF NLP IN TEXT RETRIEVAL? / Karen Sparck Jones = 1

 1 Introduction = 1

 2 Linguistically-motivated indexing = 2

  2.1 Basic Concepts = 2

  2.2 Complex Descriptions and Terms = 6

 3 Research and tests = 9

  3.1 Phase 1 : Experiments from the 1960s to the 1980s = 10

  3.2 Phase 2 : The Nineties = 16

  3.3 The TREC Programme = 17

 4 Other roles for NLP = 21

2 NLP FOR TERM VARIANT EXTRACTION : SYNERGY BETWEEN MORPHOLOGY, LEXICON, AND SYNTAX / Christian Jacquemin ; Evelyne Tzoukermann = 25

 1 Introduction : From Term Conflation to Linguistic Analysis of Term Variants = 26

  1.1 From Syntactic Variants to Morpho-syntactic Variants = 27

 2 Controlled Indexing and Term Variant Extraction = 28

  2.1 Conflation of Single Word Terms = 29

  2.2 Multi-word Term Conflation = 31

  2.3 Purpose of this Chapter = 34

 3 An Architecture for Controlled Indexing = 35

  3.1 Morphological Analysis = 35

  3.2 Multi-word Term Extraction and Conflation = 38

 4 Morphological Analysis = 38

  4.1 Inflectional Analysis = 41

  4.2 Morpho-syntactic Disambiguation = 43

  4.3 Derivational Analysis = 47

  4.4 System Implementation = 50

  4.5 Advantages of Overgeneraiting = 50

 5 FASTR : A Tool for Term Variant Extraction = 52

  5.1 A Grammar of Terms arid a Metagrammar of Transformations = 52

  5.2 A Metagrammar for Syntactic Variants = 54

  5.3 A Metagrammar for Morpho-syntactic Variants = 56

  5.4 A Method for the Formulation of Metarules = 60

  5.5 Evaltiation = 66

 6 Conclusion = 70

3 COMBINING CORPUS LINGUISTICS AND HUMAN MEMORY MODELS FOR AUTOMATIC TERM ASSOCIATION / Gerda Ruge = 75

 1 Introduction = 75

 2 Various Approaches to Automatic Term Association = 77

  2.1 Term Association by Statistic Corpus Analysis = 78

  2.2 Term Association by Linguistically Based Corpus Analysis = 79

 3 Human Memory Models = 81

  3.1 A Well Known Memory Model = 81

  3.2 A Memory Model Explaining Human Recall Capability = 82

 4 Associationism and Term Association = 85

 5 Spreading Activation with Heads and Modifiers = 88

  5.1 Spreading Activation on the Basis of Heads and Modifiers = 88

  5.2 Indirect Activation of Semantically Similar Words = 89

  5.3 Taking into account Synonymous Heads and Modifiers = 90

 6 Experiments = 92

  6.1 Test Data = 92

  6.2 Parameters of the Network = 92

  6.3 Similarity Measure = 94

  6.4 Results = 94

 7 Valuation of the Spreading Activation Approach = 95

4 USING NLP OR NLP RESOURCES FOR INFORMATION RETRIEVAL TASKS / Alan F. Smeaton = 99

 1 Introduction = 99

 2 Early Experiments = 100

 3 Using Natural Language Processes or NLP Resources = 103

 4 Using WordNet for Information Retrieval = 105

 5 Status and Plans = 109

5 EVALUATING NATURAL LANGUAGE PROCESSING TECHNIQUES IN INFORMATION RETRIEVAL / Tomek Strzalkowski ; Fang Lin ; Jin Wang ; Jose Perez-Carballo = 113

 1 Introduction and Motivation = 113

 2 NLP - Based Indexing in Information Retrieval = 116

 3 NLP in Information Retrieval : A Perspective = 118

 4 Stream-based Information Retrieval Model = 121

 5 Advanced Linguistic Streams = 123

  5.1 Head + Modifier Pairs Stream = 123

  5.2 Simple, Noun Phrase Stream = 128

  5.3 Name Stream = 129

  5.4 Other Streams = 130

 6 Stream Merging and Weighting = 133

  6.1 Inter-stream merging using score calculation = 133

  6.2 Inter stream merging using precision distribution estimates = 135

  6.3 Stream coefficients = 136

 7 Query Expansion Experiments = 136

  7.1 Why Query Expansion? = 136

  7.2 Guidelines for manual query expansion = 138

  7.3 Automatic Query Expansion = 139

 8 Summary of Results = 140

  8.1 Ad-hoc runs = 140

  8.2 Routing Runs = 141

 9 Conclusions = 142

6 STYLISTIC EXPERIMENTS IN INFORMATION RETRIEVAL / Jussi Karlgren = 147

 1 Stylistics = 147

 2 Materials and Processing = 148

  2.1 Experiments Performed = 148

  2.2 Corpus = 148

  2.3 Variables Examined = 149

  2.4 On Non-Parametric Multivariate Statistics = 153

  2.5 Correlation Between Variables = 153

 3 Visualizing Stylistic Variation = 153

 4 Stylistics and Relevance = 158

  4.1 Relevance Judgments = 158

  4.2 Relevance of Stylistics to Relevance = 159

  4.3 Hypotheses = 160

  4.4 Results and Discussion = 160

 5 Stylistics and Precision = 161

 6 Further Work = 163

7 EXTRACTION-BASED TEXT CATEGORIZATION : GENERATING DOMAIN-SPECIFIC ROLE RELATIONSHIPS AUTOMATICALLY / Ellen Riloff ; Jeffrey Lorenzen = 167

 1 Introduction = 167

 2 Extraction-based text categorization = 169

  2.1 Extraction patterns = 169

  2.2 Relevancy signatures = 172

  2.3 Augmented relevancy signatures = 175

 3 Automatically generating extraction patterns = 177

 4 Word-augmented relevancy signatures = 179

  4.1 Rilly Automatic Text Categorization = 181

 5 Experimental results = 182

  5.1 The Terrorism Category = 182

  5.2 The Attack Category = 185

  5.3 The Bombing Category = 188

  5.4 The Kidnapping Category = 191

  5.5 Comparing automatic and hand-crafted dictionaries = 193

 6 Conclusions = t94

8 LASIE JUMPS THE GATE / Yorick Wilks ; Robert Gaizauskas = 197

 1 Introduction = 197

 2 Background = 199

  2.1 TIPSTER = 200

 3 GATE Design = 201

  3.1 GDM = 202

  3.2 CREOLE = 202

  3.3 GGI = 204

 4 LASIE : An Application In GATE = 205

  4.1 Significant Features = 207

  4.2 LaSIE Modules = 207

  4.3 System Performance = 209

 5 Other IE systeryis and modules within GATE = 210

 6 The European IE scene = 211

 7 Limitations of IE systems = 212

 8 Conclusion = 213

9 PHRASAL TERMS IN REAL-WORLD IR APPLICATIONS / Joe Zhou = 215

 1 Introduction = 215

 2 Phrasing/Proximity in IR : A Compatibility Study = 217

  2.1 Method = 217

  2.2 Empirical Data Input = 219

  2.3 Empirical Data Output = 220

  2.4 Evaluation = 221

  2.5 Claims = 224

 3 Automatic Suggestion of Key Terms = 225

  3.1 Introduction = 225

  3.2 Methodology = 227

  3.3 Results and Discussion = 231

 4 Information Retrieval Applications = 247

  4.1 Introduction = 247

  4.2 Document Surrogater : A Summarization Prototype = 248

  4.3 Document Sampler : A Categorization Prototype = 253

 5 Conclusion = 257

10 NAME RECOGNITION AND RETRIEVAL PERFORMANCE / Paul Thompson ; Christopher Dozier = 261

 1 Introduction = 261

 2 Definitions, Problems, and Issues = 262

 3 The Study = 264

  3.1 Name Recognition Accuracy = 264

  3.2 Evaluation of Name Recognition and Retrieval Performance = 264

  3.3 Name Recognition Case Law Collection = 265

 4 Results = 266

  4.1 Name, Recognition Accuracy = 266

  4.2 Effect on Retrieval Performance = 267

  4.3 Name Frequencies in the Case Law Collection = 267

 5 Discussion = 268

 6 Conclusions = 270

 A The 38 Case Law Queries with Names Highlighted = 272

11 COLLAGE : AN NLP TOOLSET TO SUPPORT BOOLEAN RETRIEVAL / Jim Cowie = 273

 1 Introduction = 273

 2 Objectives = 274

 3 Rube Goldberg(or Heath Robinson) Recipe = 275

 4 Laiigiiage Processing Technology = 277

 5 Query Algebra = 277

 6 Topic Structuring = 278

  6.1 Name Recognition = 278

  6.2 Noun Phrase Recognition = 279

 7 Topic Parsing = 279

 8 Query Generation = 281

 9 Document Ranking = 281

 10 BRS/SEARCH(t) = 282

 11 Lexical Resources = 283

 12 Wordnet = 283

 13 Transfer Lexicons = 283

 14 Standard Soiirce Lookup = 284

 15 Bi-grain generation = 285

 16 Further Work = 286

12 DOCUMENT CLASSIFICATION AND ROUTING / Louise Guthrie ; Joe Guthrie ; James Leistensnider = 289

 1 Background = 289

  1.1 Meaning of a Text = 290

  1.2 Flavor of a Text = 290

 2 Introduction = 291

  2.1 The Intuitive Model = 291

  2.2 Routing vs. Classification vs. Retrieval = 292

  2.3 The Relevance of a Topic = 293

  2.4 Some Approaches = 294

  2.5 Overview of this Paper = 294

 3 Application of the Multinomial Distribution to Classification = 295

  3.1 Flavors = 295

  3.2 Word Selection = 296

  3.3 A Simple Test = 297

 4 Application of the Multinomial Distribution to Routing = 297

  4.1 Word Selection = 298

  4.2 Zero Word Counts = 299

  4.3 Routing System Performance = 299

  4.4 Document Frequency Measure = 300

  4.5 Boolean Test = 301

  4.6 The TREC5 Evaluation = 303

 5 Application to Retrospective Retrieval = 303

 6 Conclusions = 304

 A Appendix = 304

  A.1 Use of the Multinomial Distribution = 304

  A.2 Routing Using the Multinomial Distribution = 306

13 MURAX : FINDING AND ORGANIZING ANSWERS FROM TEXT SEARCH / Julian Kupiec = 311

 1 Introduction = 311

  1.1 Corpus-Based Analysis = 312

  1.2 Demand-Based Analysis = 312

 2 Exploiting the Query = 313

 3 Murax = 314

  3.1 An Example = 314

  3.2 Primary Document Matching = 316

  3.3 Answer Extraction = 316

 4 Question Characteristics = 317

 5 System Architecture = 319

  5.1 Primary Queries = 319

  5.2 Primary Query Construction = 321

  5.3 Index Organization = 323

  5.4 Scoring Primary Matches = 323

 6 Extracting Answers = 324

  6.1 Equivalent Hypotheses = 324

  6.2 Scoring Equivalent Hypotheses = 325

  6.3 Verifying Implied Expectations = 326

  6.4 Combining Evidence for Answer Hypotheses = 328

  6.5 Secondary Queries = 329

 7 Discussion = 331

 8 Conclusions = 331

14 THE USE OF CATEGORIES AND CLUSTERS FOR ORGANIZING RETRIEVAL RESULTS / Marti Hearst = 333

 1 Introduction = 333

 2 Preliminaries = 336

  2.1 Meta-Data = 336

  2.2 Definitions = 337

  2.3 The Collection TextBed = 338

 3 Using Categories to Organize Documents = 340

  3.1 Examples of MeSH Category Assignments = 342

  3.2 User Interfaces for Category Organization = 343

  3.3 Relationship of Categories to Ad Hoc and Standing Queries = 350

 4 Using Clusters to Organize Documents = 352

  4.1 Text Clustering Algorithms = 352

  4.2 Cluster Example 1 = 354

  4.3 Cluster Example 2 = 356

  4.4 Some Characteristics of Clustering Retrieval Results = 359

  4.5 Applying Clustering to Ad Hoc Queries = 362

  4.6 Graphical Displays of Text Clusters = 362

 5 Relationships between Categories and Clusters = 363

  5.1 Supervised vs. Unsupervised Algorithms = 364

  5.2 Comparing DynaCat to Clustering = 367

  5.3 Using Results of Clustering as a Category Hierarchy = 367

 6 Conclusions = 369

INDEX = 375



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