Iuliia Arsenteva
2025
Non-directive corpus annotation to reveal individual perspectives with underspecified guidelines: the case of mental workload
Iuliia Arsenteva
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Caroline Dubois
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Philippe Le Goff
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Sylvie Plantin
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Ludovic Tanguy
Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP
This paper investigates personal perceptions of mental workload through an innovative, non-directive corpus annotation method, allowing individuals of diverse profiles to define their own dimensions of annotation based on their personal perception. It contrasts with traditional approaches guided by explicit objectives and strict guidelines. Mental workload, a multifaceted concept in psychology, is characterized through various academic definitions and models. Our research, aligned with the principles of the perspectivist approach, aims to examine the degree to which individuals share a common understanding of this concept when reading the same texts. It seeks to compare the corpus produced by this non-directive annotation method. The participants, mainly employees of a large French enterprise and some academic experts on mental workload, were given the freedom to propose labels and annotate a set of texts. The experimental protocol revealed notable similarities in labels, segments, and overall annotation behavior, despite the absence of predefined guidelines. These findings suggest that individuals, given the freedom, tend to develop overlapping representations of mental workload. Furthermore, they demonstrate how non-directive annotation can uncover shared and diverse perceptions of complex concepts like mental workload, contributing to a richer understanding of how such perceptions are constructed across different individuals.