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arXiv · 2407.11371

Estimating Agreement by Chance for Sequence Annotation

Diya Li·Carolyn Rosé·Ao Yuan·Chunxiao Zhou
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Abstract

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.

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Diya Li, Carolyn Rosé, Ao Yuan, Chunxiao Zhou. 2024-07-16. Estimating Agreement by Chance for Sequence Annotation. https://arxiv.org/abs/2407.11371

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