Empirical Methodology for Crowdsourcing Ground Truth

TL;DR

This paper introduces an empirical methodology using CrowdTruth metrics to improve the quality of crowdsourced ground truth data by capturing annotator disagreement across diverse domains and tasks.

Contribution

It presents a novel empirical approach that emphasizes disagreement measurement for better ground truth quality in crowdsourcing, challenging the reliance on majority voting.

Findings

Measuring disagreement improves data quality.

More crowd workers lead to more stable annotations.

CrowdTruth metrics outperform majority vote in diverse tasks.

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, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. 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.