Empirical Methodology for Crowdsourcing Ground Truth

Tracking #: 1569-2781

This paper is currently under review
Authors: 
Anca Dumitrache
Oana Inel
Benjamin Timmermans
Carlos Ortiz
Robert-Jan Sips
Lora Aroyo

Responsible editor: 
Guest Editors Human Computation and Crowdsourcing

Submission type: 
Full Paper
Abstract: 
The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, ambiguity in the data, as well as a multitude of perspectives of the information examples are continuously present. In this paper we present an empirically derived methodology for efficiently gathering of ground truth data in a number of diverse use cases that cover a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics, capturing inter-annotator disagreement. In this paper, we show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: medical relation extraction, Twitter event identification, news event extraction and sound interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.
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