Interannotator

An interannotator is an individual who participates in the labeling or annotation of data within a multi-annotator project. Interannotators are typically used in research and development settings to create labeled datasets for natural language processing, computer vision, bioinformatics, and related fields. The work is guided by a defined annotation schema or guidelines to promote consistency across annotators.

In typical workflows, multiple interannotators independently label the same data items according to the prescribed schema. After labeling, disagreements are identified and resolved through adjudication or consensus processes, or a curated gold standard may be created for reference. The goal is to produce reliable annotations that can be used to train and evaluate automated systems.

Interannotator agreement (IAA) is a central concept in projects involving interannotators. IAA measures the reliability and consistency of labeling across annotators. Common metrics include percent agreement, Cohen’s kappa for two annotators, Fleiss’ kappa for more than two annotators, and Krippendorff’s alpha for various data types and task structures. Higher IAA suggests clearer guidelines and more reliable data, while lower IAA indicates potential ambiguities in the task or insufficient training.

Applications of interannotator efforts span corpus construction for linguistic and NLP research, image and video labeling, medical coding, and other domains that require human-derived ground truth. Challenges include subjectivity, ambiguity in definitions, need for comprehensive training, annotation fatigue, and cost. Mitigation strategies involve clear, examples-rich guidelines, pilot labeling phases, ongoing calibration sessions, and structured adjudication procedures.

See also: inter-annotator agreement, annotation guidelines, adjudication. Tools such as Brat, WebAnno, and doccano are commonly used to manage multi-annotator projects.