Manual corpus annotation is getting widely used in Natural Language Processing (NLP). While being recognized as a difficult task, no in-depth analysis of its complexity has been performed yet. We provide in this article a grid of analysis of the different complexity dimensions of an annotation task, which helps estimating beforehand the difficulties and cost of annotation campaigns. We observe the applicability of this grid on existing annotation campaigns and detail its application on a real-world example.
['Kar"en Fort', 'Adeline Nazarenko', 'Sophie Rosset']
Modeling the Complexity of Manual Annotation Tasks: a Grid of Analysis