Empirical Methodology for Crowdsourcing Ground Truth

Tracking #: 1739-2951

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Anca Dumitrache
Oana Inel
Benjamin Timmermans1
Carlos Ortiz
Robert-Jan Sips1
Lora Aroyo
Chris Welty1

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Guest Editors Human Computation and Crowdsourcing

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Full Paper
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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