Towards a Standardized, Fine-grained Manual Annotation Protocol for Verbal Fluency Data

Gabriel McKee, Joël Macoir, Lydia Gagnon, Pascale Tremblay


Abstract
We propose a new method for annotating verbal fluency data, which allows the reliable detection of the age-related decline of lexical access capacity. The main innovation is that annotators should inferentially assess the intention of the speaker when producing a word form during a verbal fluency test. Our method correlates probable speaker inten-tions such as “intended as a valid answer” or “intended as a meta-comment” with lin-guistic features such as word intensity (e.g. reduced intensity suggests private speech) and syntactic integration. The annotation scheme can be implemented with high reliabil-ity, and minimal linguistic training. When fluency data are annotated using this scheme, a relation between fluency and age emerges; this is in contrast to a strict implementation of the traditional method of annotating verbal fluency data, which has no way of deal-ing with score-confounding phenomena because it force-groups all verbal fluency pro-ductions –regardless of speaker intention— into one of three taxonomic groups (i.e. val-id answers, perseverations, and intrusions). The traditional lack of fine-grained annota-tion units is especially problematic when analyzing the qualitatively distinct fluency da-ta of older participants and may cause studies to miss the relation between lexical access capacity and age.
Anthology ID:
2020.law-1.15
Volume:
Proceedings of the 14th Linguistic Annotation Workshop
Month:
December
Year:
2020
Address:
Barcelona, Spain
Venue:
LAW
SIG:
SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
160–166
Language:
URL:
https://aclanthology.org/2020.law-1.15
DOI:
Bibkey:
Cite (ACL):
Gabriel McKee, Joël Macoir, Lydia Gagnon, and Pascale Tremblay. 2020. Towards a Standardized, Fine-grained Manual Annotation Protocol for Verbal Fluency Data. In Proceedings of the 14th Linguistic Annotation Workshop, pages 160–166, Barcelona, Spain. Association for Computational Linguistics.
Cite (Informal):
Towards a Standardized, Fine-grained Manual Annotation Protocol for Verbal Fluency Data (McKee et al., LAW 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.law-1.15.pdf