Natural Language Annotation For Machine Learning A Guide To Corpus ... [Pustejovsky & Stubbs 2012

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Copyright
Table of Contents
Preface
- Natural Language Annotation for Machine Learning
- Audience
- Organization of This Book
- Software Requirements
- Conventions Used in This Book
- Using Code Examples
- Safari® Books Online
- How to Contact Us
- Acknowledgments
  - James Adds:
  - Amber Adds:
Chapter 1. The Basics
- The Importance of Language Annotation
  - The Layers of Linguistic Description
  - What Is Natural Language Processing?
- A Brief History of Corpus Linguistics
- Language Data and Machine Learning
- The Annotation Development Cycle
- Summary
Chapter 2. Defining Your Goal and Dataset
- Defining Your Goal
  - The Statement of Purpose
  - Refining Your Goal: Informativity Versus Correctness
- Background Research
- Assembling Your Dataset
- The Size of Your Corpus
  - Existing Corpora
  - Distributions Within Corpora
- Summary
Chapter 3. Corpus Analytics
- Basic Probability for Corpus Analytics
  - Joint Probability Distributions
  - Bayes Rule
- Counting Occurrences
  - Zipf’s Law
  - N-grams
- Language Models
- Summary
Chapter 4. Building Your Model and Specification
- Some Example Models and Specs
- Adopting (or Not Adopting) Existing Models
- Different Kinds of Standards
- Summary
Chapter 5. Applying and Adopting Annotation Standards
- Metadata Annotation: Document Classification
  - Unique Labels: Movie Reviews
  - Multiple Labels: Film Genres
- Text Extent Annotation: Named Entities
- Linked Extent Annotation: Semantic Roles
- ISO Standards and You
- Summary
Chapter 6. Annotation and Adjudication
- The Infrastructure of an Annotation Project
- Specification Versus Guidelines
- Be Prepared to Revise
- Preparing Your Data for Annotation
- Writing the Annotation Guidelines
- Annotators
- Choosing an Annotation Environment
- Evaluating the Annotations
- Creating the Gold Standard (Adjudication)
- Summary
Chapter 7. Training: Machine Learning
- What Is Learning?
- Defining Our Learning Task
- Classifier Algorithms
- Sequence Induction Algorithms
- Clustering and Unsupervised Learning
- Semi-Supervised Learning
- Matching Annotation to Algorithms
- Summary
Chapter 8. Testing and Evaluation
- Testing Your Algorithm
- Evaluating Your Algorithm
- Problems That Can Affect Evaluation
- Final Testing Scores
- Summary
Chapter 9. Revising and Reporting
- Revising Your Project
- Reporting About Your Work
- Summary
Chapter 10. Annotation: TimeML
- The Goal of TimeML
- Related Research
- Building the Corpus
- Model: Preliminary Specifications
- Annotation: First Attempts
- Model: The TimeML Specification Used in TimeBank
- Annotation: The Creation of TimeBank
- TimeML Becomes ISO-TimeML
- Modeling the Future: Directions for TimeML
- Summary
Chapter 11. Automatic Annotation: Generating TimeML
- The TARSQI Components
- Improvements to the TTK
- TimeML Challenges: TempEval-2
  - TempEval-2: System Summaries
  - Overview of Results
- Future of the TTK
- Summary
Chapter 12. Afterword: The Future of Annotation
- Crowdsourcing Annotation
- Handling Big Data
- NLP Online and in the Cloud
- And Finally...
Appendix A. List of Available Corpora and Specifications
- Corpora
- Specifications, Guidelines, and Other Resources
- Representation Standards
Appendix B. List of Software Resources
- Annotation and Adjudication Software
- Machine Learning Resources
Appendix C. MAE User Guide
- Installing and Running MAE
- Loading Tasks and Files
- Saving Files
- Defining Your Own Task
- Frequently Asked Questions
Appendix D. MAI User Guide
- Installing and Running MAI
- Loading Tasks and Files
  - Loading a Task
  - Loading Files
- Adjudicating
- Saving Files
Appendix E. Bibliography
- References for Using Amazon’s Mechanical Turk/Crowdsourcing
Index
About the Authors

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