@inproceedings{rehbein-ruppenhofer-2017-detecting,
title = "Detecting annotation noise in automatically labelled data",
author = "Rehbein, Ines and
Ruppenhofer, Josef",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1107",
doi = "10.18653/v1/P17-1107",
pages = "1160--1170",
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.",
}
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%0 Conference Proceedings
%T Detecting annotation noise in automatically labelled data
%A Rehbein, Ines
%A Ruppenhofer, Josef
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F rehbein-ruppenhofer-2017-detecting
%X 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.
%R 10.18653/v1/P17-1107
%U https://aclanthology.org/P17-1107
%U https://doi.org/10.18653/v1/P17-1107
%P 1160-1170
Markdown (Informal)
[Detecting annotation noise in automatically labelled data](https://aclanthology.org/P17-1107) (Rehbein & Ruppenhofer, ACL 2017)
ACL
- Ines Rehbein and Josef Ruppenhofer. 2017. Detecting annotation noise in automatically labelled data. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1160–1170, Vancouver, Canada. Association for Computational Linguistics.