Exploiting a verb lexicon in automatic semantic role labelling
Robert Swier, Suzanne Stevenson · 2005
We develop an unsupervised semantic role labelling system that relies on the direct application of information in a predicate lexicon combined with a simple probability model. We demonstrate the usefulness of predicate lexicons for role labelling, as well as the feasibility of modifying an existing role-labelled corpus for evaluating a different set of semantic roles. We achieve a substantial improvement over an informed baseline.