Unsupervised Semantic Role Labellin

We present an unsupervised method for labelling the arguments of verbs with their semantic roles. Our bootstrapping algorithm makes initial unambiguous 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 for backing off in our probability model. We achieve 50–65% reduction in the error rate over an informed baseline, indicating the potential of our approach for a task that has heretofore relied on large amounts of manually generated training data.