Learn More
We present an unsupervised method for labelling the arguments of verbs with their semantic roles. Our bootstrapping algorithm makes initial unam-biguous role assignments, and then iteratively updates the probability model on which future assignments are based. A novel aspect of our approach is the use of verb, slot, and noun class information as the basis(More)
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(More)
  • 1