TY  - CHAP
U1  - Buchbeitrag
A1  - Rehbein, Ines
A1  - Ruppenhofer, Josef
T1  - Detecting annotation noise in automatically labelled data
T2  - Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), vol. 1 (Long Papers). July 30 - August 4, 2017 Vancouver, Canada
N2  - 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.
KW  - Computerlinguistik
KW  - Automatische Sprachverarbeitung
KW  - Annotation
KW  - Fehleranalyse
Y1  - 2017
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-80343
UR  - http://aclweb.org/anthology/P17-1107
SN  - 978-1-945626-75-3
SB  - 978-1-945626-75-3
U6  - https://doi.org/10.18653/v1/P17-1107
DO  - https://doi.org/10.18653/v1/P17-1107
SP  - 1160
EP  - 1170
PB  - The Association for Computational Linguistics
CY  - Stroudsburg PA, USA
ER  -