Improving the Science of Annotation for Natural Language Processing: The Use of the Single-Case Study for Piloting Annotation Projects
Year: 2022
Type: article
Abstract: Researchers need guidance on how to obtain maximum efficiency and accuracy when annotating training data for text classification applications. Further, given wide variability in the kinds of annotations researchers need to obtain, they would benefit from the ability to conduct low-cost experiments during the design phase of annotation projects. To this end, our study proposes the single-case study design as a feasible and causally-valid experimental design for determining the best procedures for a given annotation task. The key strength of the design is its ability to generate causal evidence at the individual level, identifying the impact of competing annotation techniques and interfaces for the specific annotator(s) included in an annotation project. In this paper, we demonstrate the application of the single-case study in an applied experiment and argue that future researchers should incorporate the design into the pilot stage of annotation projects so that, over time, a causally-valid body of knowledge regarding the best annotation techniques is built.
Source: Journal of Data Science
DOI: 10.6339/22-jds1054
Authors: Kylie Anglin, Arielle Boguslav, Todd Hall
Institutions: University of Connecticut, University of Virginia
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4285155698