TY - CHAP U1 - Konferenzveröffentlichung A1 - Rehbein, Ines A1 - Ruppenhofer, Josef T1 - Evaluating the Impact of Coder Errors on Active Learning T2 - HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies N2 - Active Learning (AL) has been proposed as a technique to reduce the amount of annotated data needed in the context of supervised classification. While various simulation studies for a number of NLP tasks have shown that AL works well on goldstandard data, there is some doubt whether the approach can be successful when applied to noisy, real-world data sets. This paper presents a thorough evaluation of the impact of annotation noise on AL and shows that systematic noise resulting from biased coder decisions can seriously harm the AL process. We present a method to filter out inconsistent annotations during AL and show that this makes AL far more robust when applied to noisy data. KW - Machine learning KW - Natural language processing KW - Active Learning Y1 - 2011 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-52929 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-52929 UR - http://dl.acm.org/citation.cfm?id=2002479&CFID=841147757&CFTOKEN=19861493 SN - 978-1-932432-87-9 SB - 978-1-932432-87-9 IS - 1 SP - 43 EP - 51 PB - Association for Computational Linguistics CY - Stroudsburg ER -