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.

本篇文章提出了使用 CrowdTruth 衡量指标来有效地收集多领域多任务的 ground truth 数据，比传统的投票方式更具优势，并研究了人数增加对标注质量的影响。

众包标注群体真相的经验方法学

Empirical Methodology for Crowdsourcing Ground Truth

Typically crowdsourcing-based approaches to gather annotated data use
inter-annotator agreement as a measure of quality. However, in many domains,
there is ambiguity in the data, as well as a multitude of perspectives of the
information examples. In this paper, we present ongoing work into the
CrowdTruth metrics, that capture and interpret inter-annotator disagreement in
crowdsourcing. The CrowdTruth metrics model the inter-dependency between the
three main components of a crowdsourcing system -- worker, input data, and
annotation. The goal of the metrics is to capture the degree of ambiguity in
each of these three components. The metrics are available online at
this https URL .

本文介绍了 CrowdTruth metrics，这是一种捕捉人群外包中标注者不一致性并对其进行解释的方法。该方法通过度量 worker，输入数据和注释三个主要组成部分之间的相互依赖关系来捕捉这种不确定度。