Artificial intelligence and legal analytics: new tools for law practice in the digital age
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York, NY ; Port Melbourne, VIC ; Delhi ; Singapore
Cambridge University Press
2017
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xiii, 426 Seiten Diagramme |
| ISBN: | 9781316622810 1316622819 9781107171503 |
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Datensatz im Suchindex
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Contents
List of illustrations page xv
List of tables xxi
Acknowledgments xxiii
PART I COMPUTATIONAL MODELS OF LEGAL REASONING 1
i Introducing AI Law and Its Role in Future Legal Practice 3
1.1. Introduction 3
1.2. AI Law and the Promise of Text Analytics 4
1.3. New Paradigms for Intelligent Technology in Legal Practice 6
1.3.1. Former Paradigm: Legal Expert Systems 8
1.3.2. Alternative Paradigms: Argument Retrieval and Cognitive
Computing 11
1.3.3. Toward the New Legal Apps 14
1.4. What Watson Can and Cannot Do 14
1.4.1. IBM's Watson 15
1.4.2. Question Answering vs. Reasoning 18
1.4.3. IBM's Debater Program 23
1.4.4. Text Analytic Tools for Legal Question Answering 27
1.4.5. Sources for Text Analytic Tools 30
1.5. A Guide to This Book 31
1.5.1. Part I: Computational Models of Legal Reasoning 32
1.5.2. Part II: Legal Text Analytics 33
1.5.3. Hk Connecting Computational Reasoning Models
and Legal Texts 34
1.6. Implications of Text Analytics for Students 35
vii
Contents
2 Modeling Statutory Reasoning 38
2.1. Introduction 38
2.2. Complexities of Modeling Statutory Reasoning 39
2.2.1. Semantic Ambiguity and Vagueness 39
2.2.2. Syntactic Ambiguity 40
2.3. Applying Statutory Legal Rules Deductively 42
2.3.1. Running a Normalized Version on a Computer 43
2.3.2. Predicate Logic 44
2.3.3. Syntactic Ambiguity as Design Constraint 45
2.3.4. The BNA Program 47
2.3.5. Some Problems of Translating Statutes into Programs 48
2.4. The Complexity of Statutory Interpretation and the Need for
Arguments 52
2.4.1. A Stepwise Process of Statutory Interpretation 53
2.4.2. Other Sources of Legal Indeterminacy 54
2.5. Management Systems for Business Rules and Processes 56
2.5.1. Business Process Expert Systems 56
2.5.2. Automating Business Process Compliance 60
2.5.3. Requirements for a Process Compliance Language 62
2.5.4. Connecting Legal Rules and Business Processes 65
2.5.5. Example of Business Process Compliance Modeling 68
2.6. Representing Statutory Networks 70
3 Modeling Case-based Legal Reasoning 73
3.1. Introduction 73
3.2. Relationship of Legal Concepts and Cases 74
3.2.1. The Legal Process 74
3.2.2. The Legal Process Illustrated 75
3.2.3. Role of Legal Concepts 76
3.3. Three Computational Models of Legal Concepts and Cases 77
3.3.1. Prototypes and Deformations 78
3.3.2. Dimensions and Legal Factors 81
3.3.3. Exemplar-based Explanations 93
3.4. Teleological Models of Case-based Legal Reasoning 97
3.5. An Approach to Modeling Teleological Reasoning 100
3.5.1. Teleology in Theory Construction 101
3.6. Design Constraints for Cognitive Computing with Case-based
Models of Legal Reasoning 104
4 Models for Predicting Legal Outcomes 107
4.1. Introduction 107
4.2. A Nearest Neighbor Approach to Automated Legal Prediction 108
Contents ix
4.3. Introduction to Supervised Machine Learning 10g
4.3.1. Machine Learning Algorithms: Decision Trees no
4.4. Predicting Supreme Court Outcomes 111
4.4.1. Features for Predicting Supreme Court Outcomes 112
4.4.2. Applying Supervised Machine Learning to SCOTUS
Data 112
4.4.3. Evaluating the Machine Learning Method 113
4.4.4. Machine Learning Evaluation Measures and Results 114
4.5. Predicting Outcomes with Case-based Arguments 114
4.5.1. Prediction with CATO 115
4.5.2. Issue-based Prediction 115
4.5.3. IBP's Prediction Algorithm 117
4.5.4. Evaluating IBP's Predictions 119
4.6. Prediction with Underlying Values 121
4.7. Prediction based on Litigation Participants and Behavior 123
4.8. Prediction in Cognitive Computing 125
5 Computational Models of Legal Argument 127
5.1. Introduction 127
5.1.1. Advantages of CMLAs 128
5.2. The Carneades Argument Model 129
5.3. An Extended Example of a CMLA in Action 131
5.3.1. Family Law Example with Carneades 132
5.3.2. Arguing with Defeasible Legal Rules 134
5.3.3. Integrating Arguing with Cases and Rules 135
5.4. Computational Model of Abstract Argumentation 139
5.5. How CMLAs Compute Winners and Losers 141
5.5.1. Resolving Conflicting Arguments about Facts 142
5.5.2. Resolving Conflicting Arguments about Values 143
5.5.3. Resolving Conflicting Arguments about Legal Rules 144
5.6. How Practical are Computational Models of Legal
Argument? 144
5.6.1. Role of Proof Standards in CMLAs 145
5.6.2. Integrating Probabilistic Reasoning into CMLAs 147
5.7. Value Judgment-based Argumentative Prediction Model 149
5.7.1. VJAP Domain Model 150
5.7.2. VJAP Values Underlying Trade Secret Regulation 151
5.7.3. VJAP Argument Schemes 154
5.7.4. VJAP's Argument-based Predictions 156
5.7.5. VJAP Program Evaluation 158
5.8. Computational Model of Evidentiary Legal Argument 160
5.9. Computational Models of Legal Argument as a Bridge 164
X
Contents
PART II LEGAL TEXT ANALYTICS 169
6 Representing Legal Concepts in Ontologies and Type Systems 171
6.1. Introduction 171
6.2. Ontology Basics 172
6.3. Sample Legal Ontologies 174
6.3.1. The e-Court Ontology 174
6.3.2. van Kralingen's Frame-based Ontology 176
6.4. Constructing Legal Ontologies 178
6.5. Ontological Support for Statutory Reasoning 181
6.6. Ontological Support for Legal Argumentation 185
6.6.1. A Target Application for Legal Argument Ontology 185
6.6.2. An Ontology for the Argument Microworld 190
6.6.3. Limits for Automating Legal Argumentation through
Ontological Support 198
6.6.4. Ontological Support for Cognitive Computing in Legal
Argumentation 201
6.7. Type Systems for Text Analytics 202
6.7.1. Defining a Type System 202
6.7.2. Type System Example: DeepQA 203
6.8. LUIMA: A Legal UIMA Type System 204
6.9. LUIMA Annotations can Support Conceptual Legal
Information Retrieval 208
7 Making Legal Information Retrieval Smarter 210
7.1. Introduction 210
7.2. Current Legal Information Retrieval Services 211
7.3. An Example of Using Commercial Legal IR Systems 212
7.4. How Legal IR Systems Work 214
7.5. IR Relevance Measures 216
7.5.1. Boolean Relevance Measure 216
7.5.2. Vector Space Approach to Relevance 217
7.5.3. Probabilistic Model of Relevance 218
7.6. Assessing Legal IR Systems 221
7.7. Recent Developments in Legal IR Systems 223
7.8. Comparing Legal IR and CMLAs 226
7.9. Improving Legal IR with AI Law Approaches 226
7.9.1. Integrating Legal Ontologies and IR 227
7.9.2. Integrating Legal IR and AI Law Relevance
Measures 227
Contents xi
7.9.3. Augmenting Legal IR Relevance Assessment with
Citation Networks 230
7.9.4. Detecting Concept Change 232
7.10. Conclusion 233
8 Machine Learning with Legal Texts 234
8.1. Introduction 234
8.2. Applying Machine Learning to Textual Data 234
8.3. A Basic Setup for Applying ML to Legal Texts 236
8.4. Machine Learning for e-Discovery 239
8.4.1. Litigators' Hypotheses in e-Discovery 240
8.4.2. Predictive Coding Process 241
8.4.3. Assessing Predictive Coding Effectiveness 243
8.4.4. Other Open Issues in Predictive Coding 246
8.4.5. Unsupervised Machine Learning from Text 247
8.5. Applying ML to Legal Case Texts in the History Project 248
8.5.1. History Project System Architecture 249
8.5.2. ML Algorithms: Support Vector Machines 251
8.5.3. History Project SVM 252
8.6. Machine Learning of Case Structures 253
8.7. Applying ML to Statutory Texts 254
8.7.1. Statutory Analysis 254
8.7.2. An Interactive ML Tool for Statutory Analysis 255
8.8. Toward Cognitive Computing Legal Apps 257
9 Extracting Information from Statutory and Regulatory Texts 259
9.1. Introduction 259
9.2. Research Overview Regarding Extracting Information from
Statutory Texts 260
9.3. Automatically Extracting Functional Information from Statutory
Provisions 262
9.3.1. Machine Learning to Extract Functional Types of Provisions 263
9.3.2. Text Classification Rules to Extract Functional Information 265
9.4. ML vs. KE for Statutory Information Extraction 266
9.5. Extracting Logical Rules from Statutes and Regulations 268
9.6. Extracting Requirements for Compliant Product Designs 270
9.6.1. Implementing Compliance with Extracted Regulations 272
9.6.2. Semiautomated Approaches to Improving Human
Annotation for Compliance 272
9.7. Extracting Functional Information to Compare Regulations 275
9.7.1. Machine Learning for Constructing Statutory Networks 276
9.7.2. Applying an ML Algorithm for Statutory Texts 278
280
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Contents
9.7.3. Evaluating the ML Algorithm on Statutory Texts and
Dealing with Sparse Training Data
9.7.4. Applying LUIMA to Enrich Statutory Text Representation
9.8. Conclusion
Extracting Argument-Related Information from Legal Case Texts
10.1. Introduction
10.2. Argument-Related Information in Legal Cases
10.3. Extracting Legal Argument Claims
10.3.1. Machine Learning to Classify Sentences as Propositions,
Premises, and Conclusions
10.3.2. Text Representation
10.3.3. Applying Statistical Learning Algorithms
10.3.4. Argument Grammar for Discourse Tree Structure
10.3.5. Identifying Instances of Argument Schemes
10.4. Extracting Argument-Related Legal Factors
10.4.1. Three Representations for Learning from Text
10.4.2. How Well Did SMILE Work?
10.4.3. Annotating Factor Components
10.5. Extracting Findings of Fact and Cited Legal Rules
10.5.1. Applying the LUIMA Type System
10.5.2. Preparing Gold Standard Cases
10.5.3. LUIMA-Annotate
10.5.4. Evaluating LUIMA-Annotate
10.6. Annotation of Training Data
10.6.1. Annotation in IBM Debater
10.6.2. Annotation Protocols
10.6.3. Computer-Supported Annotation Environments
PART III CONNECTING COMPUTATIONAL REASONING MODELS AND
LEGAL TEXTS
Conceptual Legal Information Retrieval for
Cognitive Computing
11.1. Introduction
11.2. State of the Art in Conceptual Legal IR
11.3. LUIMA Architecture
11.3.1. LUIMA-Search
11.3.2. Reranking Documents with LUIMA-Rerank
11.4. An Experiment to Evaluate LUIMA
11.4.1. Evaluation Metrics
11.4.2. LUIMAVs. CLIR
Contents xiii
11.5. Continuing to Transform Legal IR into AR 327
11.5.1. Connecting LARCCS and Legal IR Systems 328
11.5.2. Query*ng f°r Gases with Extended Argument-Related
Information 329
11.5.3. New Legal Annotation Types 332
11.5.4. Prospects for Annotating Expanded Legal Types 336
11.5.5. Eliciting Users’ Argument Needs 339
11.6. Conceptual Information Retrieval from Statutes 342
11.6.1. A Type System for Statutes 343
11.6.2. Network Techniques for Conceptual Legal IR 345
11.6.3. Conceptual Legal IR with Statutory Network Diagrams 346
11.7. Conclusion 349
12 Cognitive Computing Legal Apps 350
12.1. Introduction 350
12.2. New Legal Apps on the Market 351
12.2.1. Ross 351
12.2.2. Lex Machina 353
12.2.3. Ravel 353
12.3. Bridging Legal Texts and Computational Models 354
12.4. Cognitive Computing Apps for Testing Legal Hypotheses 354
12.4.1. A Paradigm for CCLAs: Legal Hypothesis-Testing 355
12.4.2. Targeted Legal Hypotheses 357
12.4.3. Operationalizing Hypotheses 359
12.4.4. Interpreting Hypotheses 361
12.5. Challenges for Cognitive Computing Legal Apps 367
12.5.1. Challenges: Automatically Annotating Legal Argument-
Related Information 368
12.5.2. Challenges: Manual Annotation of Training Instances 373
12.5.3. Challenges: Query-Interface Design 379
12.6. Detecting Opportunities for New Hypotheses and Arguments 381
12.7. What to Do Next? 384
12.8. Conclusion 390
Glossary 393
Bibliography 403
Index 421 |
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| spelling | Ashley, Kevin D. 1951- Verfasser (DE-588)1167459768 aut Artificial intelligence and legal analytics new tools for law practice in the digital age Kevin D. Ashley Cambridge ; New York, NY ; Port Melbourne, VIC ; Delhi ; Singapore Cambridge University Press 2017 xiii, 426 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Recht (DE-588)4048737-4 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Recht (DE-588)4048737-4 s DE-604 Erscheint auch als Online-Ausgabe 978-1-316-76138-0 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029808366&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ashley, Kevin D. 1951- Artificial intelligence and legal analytics new tools for law practice in the digital age Recht (DE-588)4048737-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4048737-4 (DE-588)4033447-8 |
| title | Artificial intelligence and legal analytics new tools for law practice in the digital age |
| title_auth | Artificial intelligence and legal analytics new tools for law practice in the digital age |
| title_exact_search | Artificial intelligence and legal analytics new tools for law practice in the digital age |
| title_full | Artificial intelligence and legal analytics new tools for law practice in the digital age Kevin D. Ashley |
| title_fullStr | Artificial intelligence and legal analytics new tools for law practice in the digital age Kevin D. Ashley |
| title_full_unstemmed | Artificial intelligence and legal analytics new tools for law practice in the digital age Kevin D. Ashley |
| title_short | Artificial intelligence and legal analytics |
| title_sort | artificial intelligence and legal analytics new tools for law practice in the digital age |
| title_sub | new tools for law practice in the digital age |
| topic | Recht (DE-588)4048737-4 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Recht Künstliche Intelligenz |
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