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Journal of Data Science


Improving the Science of Annotation for Natural Language Processing: The Use of the Single-Case Study for Piloting Annotation Projects
Volume 20, Issue 3 (2022): Special Issue: Data Science Meets Social Sciences, pp. 339–357
Pub. online: 8 July 2022      Type: Data Science In Action      Open accessOpen Access
Received
11 December 2021
Accepted
20 June 2022
Published
8 July 2022

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.

Supplementary material

 Supplementary Material
The Supplementary Material includes all of the scripts and data files necessary to reproduce the results of this paper. We also include the codebook used by our annotators.

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Copyright
2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
annotation coding single-case study supervised machine learning text classification

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