Paper: Semi-Supervised Frame-Semantic Parsing for Unknown Predicates

ACL ID P11-1144
Title Semi-Supervised Frame-Semantic Parsing for Unknown Predicates
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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.