STTS 2.0? Improving the tagset for the part-of-speech-tagging of German spoken data

Abstract

Part-of-speech tagging (POS-tagging) of spoken data requires different means of annotation than POS-tagging of written and edited texts. In order to capture the features of German spoken language, a distinct tagset is needed to respond to the kinds of elements which only occur in speech. In order to create such a coherent tagset the most prominent phenomena of spoken language need to be analyzed, especially with respect to how they differ from written language. First evaluations have shown that the most prominent cause (over 50%) of errors in the existing automatized POS-tagging of transcripts of spoken German with the Stuttgart Tübingen Tagset (STTS) and the treetagger was the inaccurate interpretation of speech particles. One reason for this is that this class of words is virtually absent from the current STTS. This paper proposes a recategorization of the STTS in the field of speech particles based on distributional factors rather than semantics. The ultimate aim is to create a comprehensive reference corpus of spoken German data for the global research community. It is imperative that all phenomena are reliably recorded in future part-of-speech tag labels.