Towards Detecting Annotation Errors in Spoken Language Corpora

The issue Consistency of corpus annotation is an essential property for the many uses of annotated corpora in computational and theoretical linguistics. While some research addresses the detection of inconsistencies in part-of-speech and other positional annotation (van Halteren, 2000; Eskin, 2000; Dickinson and Meurers, 2003a), only recently has there been some work in detecting errors in syntactic and other structural annotation (Dickinson and Meurers, 2003b; Ule and Simov, 2004). Spoken language differs in many respects from written language, but to the best of our knowledge the issue of error detection in spoken language corpora has not yet been addressed. This is significant since spoken data is increasingly relevant for linguistic and computational research—and such corpora are starting to become more readily available. We address this issue in this paper, based on the variation n-gram error detection approach developed in Dickinson and Meurers (2003a). We use the German Verbmobil treebank (Hinrichs et al., 2000) as an exemplar of a spoken language corpus and discuss properties of such corpora which are relevant when adapting the variation n-gram approach to spoken language corpora.