Estimating Agreement by Chance for Sequence Annotation

• Published in: arXiv.org
• Main Authors: Li, Diya, Rosé, Carolyn, Ao Yuan, Zhou, Chunxiao
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 16.07.2024
• Subjects: Annotations; Estimation; Natural language processing; Performance assessment; Performance evaluation; Reliability

In the field of natural language processing, correction of performance assessment for chance agreement plays a crucial role in evaluating the reliability of annotations. However, there is a notable dearth of research focusing on chance correction for assessing the reliability of sequence annotation tasks, despite their widespread prevalence in the field. To address this gap, this paper introduces a novel model for generating random annotations, which serves as the foundation for estimating chance agreement in sequence annotation tasks. Utilizing the proposed randomization model and a related comparison approach, we successfully derive the analytical form of the distribution, enabling the computation of the probable location of each annotated text segment and subsequent chance agreement estimation. Through a combination simulation and corpus-based evaluation, we successfully assess its applicability and validate its accuracy and efficacy.