Preface 1 Introduction 1.1 Research Orientation 1.2 Rationale 1.3 Research Objective and Research Questions 1.4 Structure of the Book 2 Literature Review 2.1 Legal Information Processing 2.1.1 Legal Text Collection 2.1.2 Text Cleaning 2.1.3 Legal Information Database 2.2 Studies on Information Seeking 2.2.1 Information Seeking and Information Retrieval 2.2.2 Classification of Information Seeking 2.2.3 Psychological Models of Information Seeking 2.3 Studies on Automatic Text Classification 2.3.1 Preprocessing 2.3.2 Text Representation 2.3.3 Feature Selection 2.3.4 Classification 2.4 Discourse Information Theory 2.4.1 Information Units and Information Knots 2.4.2 Information Elements 2.4.3 Information Sources 2.4.4 Information Sharing Categories 2.4.5 Discourse Information Analysis 2.4.6 Information Analysis of Legal Texts 2.5 Other Studies Relevant to This Research 2.5.1 Rationalism and Empiricism in Modern Linguistic Research 2.5.2 Markov Models 2.5.3 Means-ends Analysis 2.6 Summary 3 Analytical Framework and Methodology 3.1 Background 3.1.1 Discourse Information Theory 3.1.2 The ISP Model 3.1.3 Principles of SVM 3.1.4 Principles of the Viterbi Algorithm 3.2 Theoretical Considerations 3.3 Analytical Framework 3.4 Research Methodology 3.4.1 Data Collection 3.4.2 Data Analysis 4 Information Features of Legal Texts 4.1 Introduction 4.2 Inter-type Constancy 4.2.1 Macro Information Features across Types 4.2.2 Micro Information Features across Types 4.3 Intra-type Constancy 4.3.1 Constant Macro Information Features
4.3.2 Constant Micro Information Features 4.4 Intra-type Variation 4.4.1 Variation in Macro Information Features 4.4.2 Variation in Micro Information Features 4.5 The Information Pattern of CJFIs as an Example 4.5.1 Information Knot 4.5.2 Information Sources 4.6 Summary 5 Information Seeking Based on Information Processing Rules 5.1 Introduction 5.2 Psychological Procedure of Human Beings' Information Processing 5.2.1 The Example Task 5.2.2 Task Analysis 5.2.3 Analysis by Applying Relevant Psychological Theories 5.2.4 Description of the Psychological Process 5.2.5 Construction of a Coding Scheme 5.2.6 Collection of the Think-aloud Protocols 5.2.7 Coding Protocols 5.2.8 Comparison 5.3 Automatic Text Classification 5.3.1 The Relationship between Text Classification and Macro Information Features 5.3.2 The Relationship between Text Classification and Micro Information Features 5.3.3 Realization of Automatic Text Classification 5.4 Pretreatment of Discourse Information 5.4.1 Segmentation Based on Punctuation 5.4.2 The Segmentation-free Verb Identification Model 5.4.3 Acquisition of Potential Information Units 5.4.4 The Recognition Scheme of Condition Elements 5.4.5 Recognition of Entity Elements and Identification of Information Units 5.5 Automatic Identification of Upper-level Units 5.5.1 The Relationship between Text Classification and Identification of Upper-level Units 5.5.2 The Relationship between the Governing Domain of Upper-level Units and Paragraphs 5.5.3 Identification Rules of Upper-level Information Units 5.6 Automatic Identification of Lower-level Information Units 5.6.1 The Relationship between Upper-level Units and Lower-level Units 5.6.2 Identification Rules of Lower-level Information Units 5.6.3 Realization of Information Item Identification 5.7 Presentation of Information Seeking -- an Example 5.8 Summary 6 Evaluation of Automatic Information Processing 6.1 Introduction 6.2 Evaluation of the Rule-Based Text Classification 6.2.1 An SVM-based Legal Text Classifier 6.2.2 A Comparison between IRBA and SVMBA 6.3 Evaluation of the Rule-Based Information Identification 6.3.1 A Viterbi-algorithm-based Information Identifier 6.3.2 A Comparison between IRBA and VBA 6.4 Summary 7 Major Findings and Conclusion 7.1 Summary of the Major Findings
7.2 Conclusion 7.3 Implications 7.4 Limitations and Suggestions for Further Research Appendix I The Coding Scheme in Discourse Information Seeking Appendix II Realization of Viterbi Algorithm in C++ References
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