Annotating Collaboratively -- Crowdsourcing Annotation -- Conclusion -- (Some) Annotation Tools -- Glossary -- Bibliography -- Index -- Other titles from iSTE in Cognitive Science and Knowledge Management

Preface ix List of Acronyms xi Introduction xiii Chapter 1. Annotating Collaboratively 1 1.1. The annotation process (re)visited 1 1.1.1. Building consensus 1 1.1.2. Existing methodologies 3 1.1.3. Preparatory work 7 1.1.4. Pre-campaign 13 1.1.5. Annotation 17 1.1.6. Finalization 21 1.2. Annotation complexity 24 1.2.1. Example overview 25 1.2.2. What to annotate? 28 1.2.3. How to annotate? 30 1.2.4. The weight of the context 36 1.2.5. Visualization 38 1.2.6. Elementary annotation tasks 40 1.3. Annotation tools 43 1.3.1. To be or not to be an annotation tool 43 1.3.2. Much more than prototypes 46 1.3.3. Addressing the new annotation challenges 49 1.3.4. The impossible dream tool 54 1.4. Evaluating the annotation quality 55 1.4.1. What is annotation quality? 55 1.4.2. Understanding the basics 56 1.4.3.

Includes bibliographical references and index.

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This book presents a unique opportunity for constructing a consistent image of collaborative manual annotation for Natural Language Processing (NLP). NLP has witnessed two major evolutions in the past 25 years: firstly, the extraordinary success of machine learning, which is now, for better or for worse, overwhelmingly dominant in the field, and secondly, the multiplication of evaluation campaigns or shared tasks. Both involve manually annotated corpora, for the training and evaluation of the systems. These corpora have progressively become the hidden pillars of our domain, providing food for our hungry machine learning algorithms and reference for evaluation. Annotation is now the place where linguistics hides in NLP. However, manual annotation has largely been ignored for some time, and it has taken a while even for annotation guidelines to be recognized as essential. Although some efforts have been made lately to address some of the issues presented by manual annotation, there has still been little research done on the subject. This book aims to provide some useful insights into the subject. Manual corpus annotation is now at the heart of NLP, and is still largely unexplored. There is a need for manual annotation engineering (in the sense of a precisely formalized process), and this book aims to provide a first step towards a holistic methodology, with a global view on annotation.