Modeling Annotator Expertise: Learning When Everybody Knows a Bit of Something
Abstract
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this problem when annotators may be unreliable (labels are noisy), but also their expertise varies depending on the data they observe (annotators may have knowledge about different parts of the input space). That is, an annotator may not be consistently accurate (or inaccurate) across the task domain. The presented approach produces classification and annotator models that allow us to provide estimates of the true labels and annotator variable expertise. We provide an analysis of the proposed model under various scenarios and show experimentally that annotator expertise can indeed vary in real tasks and that the presented approach provides clear advantages over previously introduced multi-annotator methods, which only consider general annotator characteristics.
Cite
Text
Yan et al. "Modeling Annotator Expertise: Learning When Everybody Knows a Bit of Something." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Yan et al. "Modeling Annotator Expertise: Learning When Everybody Knows a Bit of Something." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/yan2010aistats-modeling/)BibTeX
@inproceedings{yan2010aistats-modeling,
title = {{Modeling Annotator Expertise: Learning When Everybody Knows a Bit of Something}},
author = {Yan, Yan and Rosales, Romer and Fung, Glenn and Schmidt, Mark and Hermosillo, Gerardo and Bogoni, Luca and Moy, Linda and Dy, Jennifer},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {932-939},
volume = {9},
url = {https://mlanthology.org/aistats/2010/yan2010aistats-modeling/}
}