Task as Context: A Sensemaking Perspective on Annotating Inter-Dependent Event Attributes with Non-Experts
Keywords:Crowdsourcing, Event Extraction, Data Annotation, Sensemaking
AbstractThis paper explores the application of sensemaking theory to support non-expert crowds in intricate data annotation tasks. We investigate the influence of procedural context and data context on the annotation quality of novice crowds, defining procedural context as completing multiple related annotation tasks on the same data point, and data context as annotating multiple data points with semantic relevance. We conducted a controlled experiment involving 140 non-expert crowd workers, who generated 1400 event annotations across various procedural and data context levels. Assessments of annotations demonstrate that high procedural context positively impacts annotation quality, although this effect diminishes with lower data context. Notably, assigning multiple related tasks to novice annotators yields comparable quality to expert annotations, without costing additional time or effort. We discuss the trade-offs associated with procedural and data contexts and draw design implications for engaging non-experts in crowdsourcing complex annotation tasks.
How to Cite
Li, T., Wang, P., Shi, T., Bian, Y., & Esakia, A. (2023). Task as Context: A Sensemaking Perspective on Annotating Inter-Dependent Event Attributes with Non-Experts. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 11(1), 78-90. https://doi.org/10.1609/hcomp.v11i1.27550
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