Detecting annotation noise in automatically labelled data - Rehbein, Ines; Ruppenhofer, Josef (The 55th Annual Meeting of the Association for Computational Linguistics - proceedings of the conference ; Vol. 1: Long papers, 2017)
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.
Gesehen am 31.03.2020