While the conventional view is that there exists an unbridgable gulf between DLR development and statistical NLP, in practice an increasingly symbiotic relationship has developed between the two (Baldwin et al., 2007).
indeed, statistical NLP has provided a strong consumer base for DLRs, as illustrated by the "core" English language technologies of: • POS tagging (developed primarily using the Penn Treebank (Marcus et al., 1993)) • treebank parsing (based almost exclusively on the Penn Treebank (Marcus et al., 1993)) • word sense disambiguation (based on SemCor (Landes et al., 1998) and the Senseval tasks) • semantic role labelling (based on PropBank (Palmer et al., 2005) and FrameNet (Baker et al., 1998)) not to say that it has been a one-way street, however, and modern-day DLR developers are calling upon statistical NLP techniques in more and more of their day to day work.