🤖 AI Summary
Amid the growing diversity of natural language processing tasks, existing inter-annotator agreement (IAA) metrics often suffer from limited applicability and interpretability when confronted with heterogeneous task types, label imbalance, and missing data. This work systematically reviews the theoretical foundations and practical methodologies of IAA, offering the first structured integration of mainstream metrics—such as Cohen’s Kappa and Krippendorff’s Alpha—organized by task type. It clarifies their underlying assumptions and delineates their boundaries of applicability. Furthermore, the study proposes a reliability assessment strategy that combines confidence intervals with analysis of disagreement patterns. By providing a clear, principled guide for selecting IAA metrics, this research significantly enhances the transparency, reproducibility, and scientific rigor of human annotation and evaluation practices in the NLP community.
📝 Abstract
Human annotation remains the foundation of reliable and interpretable data in Natural Language Processing (NLP). As annotation and evaluation tasks continue to expand, from categorical labelling to segmentation, subjective judgment, and continuous rating, measuring agreement between annotators has become increasingly more complex. This paper outlines how inter-annotator agreement (IAA) has been conceptualised and applied across NLP and related disciplines, describing the assumptions and limitations of common approaches. We organise agreement measures by task type and discuss how factors such as label imbalance and missing data influence reliability estimates. In addition, we highlight best practices for clear and transparent reporting, including the use of confidence intervals and the analysis of disagreement patterns. The paper aims to serve as a guide for selecting and interpreting agreement measures, promoting more consistent and reproducible human annotation and evaluation in NLP.