Collaborative annotation for reliable natural language processing technical and sociological aspects
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...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London, England ; Hoboken, New Jersey :
ISTE
2016.
|
| Edición: | 1st edition |
| Colección: | Cognitive science series.
THEi Wiley ebooks. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631482906719 |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Contents; Preface; List of Acronyms; Introduction; 1: Annotating Collaboratively; 2: Crowdsourcing Annotation; Conclusion; Appendix: (Some) Annotation Tools; Glossary; Bibliography; Index; Other titles from ISTE in Cognitive Science and Knowledge Management; ELUA; I.1. Natural Language Processing and manual annotation: Dr Jekyll and Mr Hy|ide?; I.2. Rediscovering annotation; 1.1. The annotation process (re)visited; 1.2. Annotation complexity; 1.3. Annotation tools; 1.4. Evaluating the annotation quality; 1.5. Conclusion
- 2.1. What is crowdsourcing and why should we be interested in it?2.2. Deconstructing the myths; 2.2.3. "Crowdsourcing involves (a crowd of) non-experts"; 2.3. Playing with a purpose; 2.4. Acknowledging crowdsourcing specifics; 2.5. Ethical issues; A.1. Generic tools; A.2. Task-oriented tools; A.3. NLP annotation platforms; A.4. Annotation management tools; A.5. (Many) Other tools; I.1.1. Where linguistics hides; I.1.2. What is annotation?; I.1.3. New forms, old issues; I.2.1. A rise in diversity and complexity; I.2.2. Redefining manual annotation costs; 1.1.1. Building consensus
- 1.1.2. Existing methodologies1.1.3. Preparatory work; 1.1.4. Pre-campaign; 1.1.5. Annotation; 1.1.6. Finalization; 1.2.1. Example overview; 1.2.2. What to annotate?; 1.2.3. How to annotate?; 1.2.4. The weight of the context; 1.2.5. Visualization; 1.2.6. Elementary annotation tasks; 1.3.1. To be or not to be an annotation tool; 1.3.2. Much more than prototypes; 1.3.3. Addressing the new annotation challenges; 1.3.4. The impossible dream tool; 1.4.1. What is annotation quality?; 1.4.2. Understanding the basics; 1.4.3. Beyond kappas; 1.4.4. Giving meaning to the metrics; 2.1.1. A moving target
- 2.1.2. A massive success2.2.1. Crowdsourcing is a recent phenomenon; 2.2.2. Crowdsourcing involves a crowd (of non-experts); 2.3.1. Using the players' innate capabilities and world knowledge; 2.3.2. Using the players' school knowledge; 2.3.3. Using the players' learning capacities; 2.4.1. Motivating the participants; 2.4.2. Producing quality data; 2.5.1. Game ethics; 2.5.2. What's wrong with Amazon Mechanical Turk?; 2.5.3. A charter to rule them all; A.1.1. Cadixe; A.1.2. Callisto; A.1.3. Amazon Mechanical Turk; A.1.4. Knowtator; A.1.5. MMAX2; A.1.6. UAM CorpusTool; A.1.7. Glozz; A.1.8. CCASH
- A.1.9. bratA.2.1. LDC tools; A.2.2. EasyRef; A.2.3. Phrase Detectives; A.2.4. ZombiLingo; A.3.1. GATE; A.3.2. EULIA; A.3.3. UIMA; A.3.4. SYNC3; A.4.1. Slate; A.4.2. Djangology; A.4.3. GATE Teamware; A.4.4. WebAnno; 1.1.3.1. Identifying the actors; 1.1.3.2. Taking the corpus into account; 1.1.3.3. Creating and modifying the annotation guide; 1.1.4.1. Building the mini-reference; 1.1.4.2. Training the annotators; 1.1.5.1. Breaking-in; 1.1.5.2. Annotating; 1.1.5.3. Updating; 1.1.6.1. Failure; 1.1.6.2. Adjudication; 1.1.6.3. Reviewing; 1.1.6.4. Publication; 1.2.1.1. Example 1: POS
- 1.2.1.2. Example 2: gene renaming
