Inter-annotator Agreement

This chapter touches upon several issues in the calculation and assessment of inter annotator agreement. It gives an introduction to the theory behind agreement coefficients and examples of their application to linguistic annotation tasks. Specific examples explore variation in annotator performance due to heterogeneous data, complex labels, item difficulty, and annotator differences, showing how global agreement coefficients may mask these sources of variation, and how detailed agreement studies can give insight into both the annotation process and the nature of the underlying data. The chapter also reviews recent work on using machine learning to exploit the variation among annotators and learn detailed models from which accurate labels can be inferred. I therefore advocate an approach where agreement studies are not used merely as a means to accept or reject a particular annotation scheme, but as a tool for exploring patterns in the data that are being annotated.