Detecting annotation noise in automatically labelled data

Ines Rehbein, Josef Ruppenhofer


Abstract
We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.
Anthology ID:
P17-1107
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1160–1170
Language:
URL:
https://www.aclweb.org/anthology/P17-1107
DOI:
10.18653/v1/P17-1107
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1107.pdf