Interannotaatorluse

Interannotaatorluse, also known as inter-rater reliability or agreement, is a measure of the consistency with which two or more independent annotators assign categories or values to the same item. In fields like natural language processing, machine learning, and psychology, it is crucial to ensure that data annotation is objective and reliable. High interannotaatorluse indicates that the annotation guidelines are clear and that the annotators understand and apply them consistently.

The process typically involves having multiple annotators label a subset of data independently. Their annotations are then compared to calculate a score that quantifies their agreement. Various statistical measures exist for this, such as Cohen's Kappa, Fleiss' Kappa, or Krippendorff's Alpha. These metrics take into account the possibility of agreement occurring by chance.

Low interannotaatorluse can signal several issues. The annotation guidelines might be ambiguous, requiring refinement. The annotators may have received insufficient training, or there might be inherent subjectivity in the coding scheme that makes perfect agreement difficult to achieve. Addressing low interannotaatorluse is essential before proceeding with large-scale annotation projects, as it directly impacts the quality and trustworthiness of the resulting dataset. Strategies to improve it include clarifying guidelines, conducting further training, and resolving disagreements through discussion or by involving a senior annotator.