A number of recent articles in computational linguistics venues called for a closer examination of the type of noise present in annotated datasets used for benchmarking (Reidsma and Carletta, 2008; Beigman Klebanov and Beigman, 2009). In particular, Beigman Klebanov and Beigman articulated a type of noise they call annotation noise and showed that in worst case such noise can severely degrade the generalization ability of a linear classifier (Beigman and Beigman Klebanov, 2009). In this paper, we provide quantitative empirical evidence for the existence of this type of noise in a recently benchmarked dataset. The proposed methodology can be used to zero in on unreliable instances, facilitating generation of cleaner gold standards for benchmarking.
['Beata Beigman Klebanov', 'Eyal Beigman']
Some Empirical Evidence for Annotation Noise in a Benchmarked Dataset