Semi-supervised frame-semantic parsing for unknown predicates

We describe a new approach to disambiguat-ing semantic frames evoked by lexical predi-cates previously unseen in a lexicon or anno-tated data. Our approach makes use of large amounts of unlabeled data in a graph-based semi-supervised learning framework. We con-struct a large graph where vertices correspond to potential predicates and use label propa-gation to learn possible semantic frames for new ones. The label-propagated graph is used within a frame-semantic parser and, for un-known predicates, results in over 15 % abso-lute improvement in frame identification ac-curacy and over 13 % absolute improvement in full frame-semantic parsing F1 score on a blind test set, over a state-of-the-art supervised baseline.