Natural Language Annotation for Machine Learning, Volume 9,Halaman 878"O'Reilly Media, Inc.", 2013 - 326 halaman Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project.
This book is a perfect companion to O’Reilly’s Natural Language Processing with Python. |
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Chapter 1 The Basics | 1 |
Chapter 2 Defining Your Goal and Dataset | 33 |
Chapter 3 Corpus Analytics | 53 |
Chapter 4 Building Your Model and Specification | 67 |
Chapter 5 Applying and Adopting Annotation Standards | 87 |
Chapter 6 Annotation and Adjudication | 105 |
Machine Learning | 139 |
Chapter 8 Testing and Evaluation | 169 |
Generating TimeML | 219 |
The Future of Annotation | 239 |
Appendix A List of Available Corpora and Specifications | 249 |
Appendix B List of Software Resources | 271 |
Appendix C MAE User Guide | 291 |
Appendix D MAI User Guide | 299 |
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accuracy adjudicated annotation guidelines annotation Language annotation project annotation task annotation tool ATTLIST attribute bigram Brown Corpus calculate CDATA chapter classification task classifier Cohen's Kappa computational linguistics corpora Corpus Modality create Crowdsourcing dataset defined discussed document elements encoding English URL evaluation event example extent tags F-measure film format genre goal gold standard IAA scores identify IDREF input James Pustejovsky label Language-independent URL learning algorithm link tags look machine learning MAMA cycle MATTER cycle MaxEnt ML algorithms movie reviews n-gram Named Entity Natural Language Natural Language Processing negative NLTK noun part-of-speech tagging perform POS tagging probability problem Pustejovsky relationships rule-based semantic role Semi-supervised learning sentence sequence spec specification stand-off annotation supervised learning syntactic TARSQI temporal expression TimeBank TimeML TIMEX3 TLINK token TreeBank types Various URL verb words Written Language