Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining


Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining (2015) .. by Henning Wachsmuth


Contents

1 Introduction … 1
1.1 Information Search in Times of Big Data … 1
1.1.1 Text Mining to the Rescue … 2
1.2 A Need for Efficient and Robust Text Analysis Pipelines … 4
1.2.1 Basic Text Analysis Scenario … 5
1.2.2 Shortcomings of Traditional Text Analysis Pipelines … 6
1.2.3 Problems Approached in This Book … 7
1.3 Towards Intelligent Pipeline Design and Execution … 8
1.3.1 Central Research Question and Method … 8
1.3.2 An Artificial Intelligence Approach … 9
1.4 Contributions and Outline of This Book … 12
1.4.1 New Findings in Ad-Hoc Large-Scale Text Mining … 13
1.4.2 Contributions to the Concerned Research Fields … 14
1.4.3 Structure of the Remaining Chapters … 15
1.4.4 Published Research Within This Book … 16

2 Text Analysis Pipelines … 19
2.1 Foundations of Text Mining… 20
2.1.1 Text Mining … 20
2.1.2 Information Retrieval… 21
2.1.3 Natural Language Processing … 22
2.1.4 Data Mining … 25
2.1.5 Development and Evaluation … 30
2.2 Text Analysis Tasks, Processes, and Pipelines … 34
2.2.1 Text Analysis Tasks … 34
2.2.2 Text Analysis Processes … 36
2.2.3 Text Analysis Pipelines … 37
2.3 Case Studies in This Book … 40
2.3.1 InfexBA – Information Extraction for Business Applications … 40
2.3.2 ArguAna – Argumentation Analysis in Customer Opinions … 42
2.3.3 Other Evaluated Text Analysis Tasks.. 43
2.4 State of the Art in Ad-Hoc Large-Scale Text Mining.. 44
2.4.1 Text Analysis Approaches … 44
2.4.2 Design of Text Analysis Approaches … 45
2.4.3 Efficiency of Text Analysis Approaches… 47
2.4.4 Robustness of Text Analysis Approaches … 50

3 Pipeline Design … 55
3.1 Ideal Construction and Execution for Ad-Hoc Text Mining … 56
3.1.1 The Optimality of Text Analysis Pipelines … 56
3.1.2 Paradigms of Designing Optimal Text Analysis Pipelines… 60
3.1.3 Case Study of Ideal Construction and Execution … 64
3.1.4 Discussion of Ideal Construction and Execution … 68
3.2 A Process-Oriented View of Text Analysis … 68
3.2.1 Text Analysis as an Annotation Task.. 69
3.2.2 Modeling the Information to Be Annotated… 70
3.2.3 Modeling the Quality to Be Achieved by the Annotation … 71
3.2.4 Modeling the Analysis to Be Performed for Annotation … 72
3.2.5 Defining an Annotation Task Ontology … 74
3.2.6 Discussion of the Process-Oriented View … 75
3.3 Ad-Hoc Construction via Partial Order Planning … 76
3.3.1 Modeling Algorithm Selection as a Planning Problem … 77
3.3.2 Selecting the Algorithms of a Partially Ordered Pipeline … 78
3.3.3 Linearizing the Partially Ordered Pipeline.. 80
3.3.4 Properties of the Proposed Approach … 82
3.3.5 An Expert System for Ad-Hoc Construction … 86
3.3.6 Evaluation of Ad-Hoc Construction … 88
3.3.7 Discussion of Ad-Hoc Construction… 91
3.4 An Information-Oriented View of Text Analysis … 92
3.4.1 Text Analysis as a Filtering Task … 92
3.4.2 Defining the Relevance of Portions of Text … 95
3.4.3 Specifying a Degree of Filtering for Each Relation Type … 97
3.4.4 Modeling Dependencies of the Relevant Information Types . . 98
3.4.5 Discussion of the Information-Oriented View … 100
3.5 Optimal Execution via Truth Maintenance … 101
3.5.1 Modeling Input Control as a Truth Maintenance Problem… 101
3.5.2 Filtering the Relevant Portions of Text… 104
3.5.3 Determining the Relevant Portions of Text … 106
3.5.4 Properties of the Proposed Approach … 107
3.5.5 A Software Framework for Optimal Execution … 109
3.5.6 Evaluation of Optimal Execution.. 111
3.5.7 Discussion of Optimal Execution.. 116
3.6 Trading Efficiency for Effectiveness in Ad-Hoc Text Mining … 117
3.6.1 Integration with Passage Retrieval … 117
3.6.2 Integration with Text Filtering … 118
3.6.3 Implications for Pipeline Efficiency … 120

4 Pipeline Efficiency … 123
4.1 Ideal Scheduling for Large-Scale Text Mining … 124
4.1.1 The Efficiency Potential of Pipeline Scheduling … 124
4.1.2 Computing Optimal Schedules with Dynamic Programming … 126
4.1.3 Properties of the Proposed Solution … 129
4.1.4 Case Study of Ideal Scheduling… 131
4.1.5 Discussion of Ideal Scheduling … 134
4.2 The Impact of Relevant Information in Input Texts … 134
4.2.1 Formal Specification of the Impact … 135
4.2.2 Experimental Analysis of the Impact … 136
4.2.3 Practical Relevance of the Impact … 138
4.2.4 Implications of the Impact … 140
4.3 Optimized Scheduling via Informed Search … 141
4.3.1 Modeling Pipeline Scheduling as a Search Problem … 142
4.3.2 Scheduling Text Analysis Algorithms with k-best A Search … 144
4.3.3 Properties of the Proposed Approach … 147
4.3.4 Evaluation of Optimized Scheduling … 149
4.3.5 Discussion of Optimized Scheduling … 155
4.4 The Impact of the Heterogeneity of Input Texts … 156
4.4.1 Experimental Analysis of the Impact … 156
4.4.2 Quantification of the Impact … 159
4.4.3 Practical Relevance of the Impact … 161
4.4.4 Implications of the Impact … 163
4.5 Adaptive Scheduling via Self-supervised Online Learning … 163
4.5.1 Modeling Pipeline Scheduling as a Classification Problem … 164
4.5.2 Learning to Predict Run-Times Self-supervised and Online.. 165
4.5.3 Adapting a Pipeline’s Schedule to the Input Text … 166
4.5.4 Properties of the Proposed Approach … 167
4.5.5 Evaluation of Adaptive Scheduling … 169
4.5.6 Discussion of Adaptive Scheduling … 175
4.6 Parallelizing Execution in Large-Scale Text Mining … 177
4.6.1 Effects of Parallelizing Pipeline Execution … 177
4.6.2 Parallelization of Text Analyses … 179
4.6.3 Parallelization of Text Analysis Pipelines … 180
4.6.4 Implications for Pipeline Robustness … 182

5 Pipeline Robustness … 183
5.1 Ideal Domain Independence for High-Quality Text Mining … 184
5.1.1 The Domain Dependence Problem in Text Analysis … 184
5.1.2 Requirements of Achieving Pipeline Domain Independence . . 186
5.1.3 Domain-Independent Features of Argumentative Texts … 190
5.2 A Structure-Oriented View of Text Analysis … 191
5.2.1 Text Analysis as a Structure Classification Task … 191
5.2.2 Modeling the Argumentation and Content of a Text … 192
5.2.3 Modeling the Argumentation Structure of a Text… 193
5.2.4 Defining a Structure Classification Task Ontology.. 195
5.2.5 Discussion of the Structure-Oriented View … 197
5.3 The Impact of the Overall Structure of Input Texts … 198
5.3.1 Experimental Analysis of Content and Style Features… 198
5.3.2 Statistical Analysis of the Impact of Task-Specific Structure . . 201
5.3.3 Statistical Analysis of the Impact of General Structure … 204
5.3.4 Implications of the Invariance and Impact … 206
5.4 Features for Domain Independence via Supervised Clustering.. 208
5.4.1 Approaching Classification as a Relatedness Problem … 208
5.4.2 Learning Overall Structures with Supervised Clustering … 209
5.4.3 Using the Overall Structures as Features for Classification … 212
5.4.4 Properties of the Proposed Features … 214
5.4.5 Evaluation of Features for Domain Independence … 216
5.4.6 Discussion of Features for Domain Independence … 221
5.5 Explaining Results in High-Quality Text Mining.. 223
5.5.1 Intelligible Text Analysis through Explanations… 224
5.5.2 Explanation of Arbitrary Text Analysis Processes … 224
5.5.3 Explanation of the Class of an Argumentative Text … 227
5.5.4 Implications for Ad-Hoc Large-Scale Text Mining … 229

6 Conclusion … 231
6.1 Contributions and Open Problems … 232
6.1.1 Enabling Ad-Hoc Text Analysis … 232
6.1.2 Optimally Analyzing Text … 233
6.1.3 Optimizing Analysis Efficiency… 233
6.1.4 Robustly Classifying Text … 234
6.2 Implications and Outlook … 235
6.2.1 Towards Ad-Hoc Large-Scale Text Mining… 235
6.2.2 Outside the Box … 237

Appendix A Text Analysis Algorithms … 239
A.1 Analyses and Algorithms… 239
A.1.1 Classification of Text … 240
A.1.2 Entity Recognition … 242
A.1.3 Normalization and Resolution.. 242
A.1.4 Parsing … 243
A.1.5 Relation Extraction and Event Detection … 244
A.1.6 Segmentation … 246
A.1.7 Tagging … 247
A.2 Evaluation Results … 248
A.2.1 Efficiency Results … 248
A.2.2 Effectiveness Results … 249

Appendix B Software… 251
B.1 An Expert System for Ad-hoc Pipeline Construction … 251
B.1.1 Getting Started… 252
B.1.2 Using the Expert System… 253
B.1.3 Exploring the Source Code of the System … 254
B.2 A Software Framework for Optimal Pipeline Execution … 256
B.2.1 Getting Started… 256
B.2.2 Using the Framework … 256
B.2.3 Exploring the Source Code of the Framework … 257
B.3 A Web Application for Sentiment Scoring and Explanation … 258
B.3.1 Getting Started… 258
B.3.2 Using the Application … 259
B.3.3 Exploring the Source Code of the Application … 260
B.3.4 Acknowledgments … 261
B.4 Source Code of All Experiments and Case Studies … 261
B.4.1 Software … 261
B.4.2 Text Corpora … 262
B.4.3 Experiments and Case Studies … 262

Appendix C Text Corpora … 265
C.1 The Revenue Corpus… 265
C.1.1 Compilation… 265
C.1.2 Annotation … 267
C.1.3 Files.. 269
C.1.4 Acknowledgments … 269
C.2 The ArguAna TripAdvisor Corpus … 269
C.2.1 Compilation… 269
C.2.2 Annotation … 271
C.2.3 Files.. 274
C.2.4 Acknowledgments … 274
C.3 The LFA-11 Corpus … 274
C.3.1 Compilation… 275
C.3.2 Annotation … 275
C.3.3 Files.. 278
C.3.4 Acknowledgments … 278
C.4 Used Existing Text corpora … 278
C.4.1 CoNLL-2003 Dataset (English and German) … 278
C.4.2 Sentiment Scale Dataset (and Related Datasets) … 279
C.4.3 Brown Corpus … 279
C.4.4 Wikipedia Sample … 280

References… 281

Index … 293

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