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Detecting annotation noise in automatically labelled data

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 o...

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Hauptverfasser: Rehbein, Ines (VerfasserIn) , Ruppenhofer, Josef (VerfasserIn)
Dokumenttyp: Kapitel/Artikel Konferenzschrift
Sprache:Englisch
Veröffentlicht: July 2017
In: The 55th Annual Meeting of the Association for Computational Linguistics - proceedings of the conference ; Vol. 1: Long papers
Year: 2017, Pages: 1160-1170
DOI:10.18653/v1/P17-1107
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.18653/v1/P17-1107
Verlag, lizenzpflichtig, Volltext: https://www.aclweb.org/anthology/P17-1107
Volltext
Verfasserangaben:Ines Rehbein, Josef Ruppenhofer

MARC

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