  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.
