Paper: Graph Alignment for Semi-Supervised Semantic Role Labeling

ACL ID D09-1002
Title Graph Alignment for Semi-Supervised Semantic Role Labeling
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

Unknown lexical items present a major obstacle to the development of broad- coverage semantic role labeling systems. We address this problem with a semi- supervised learning approach which ac- quires training instances for unseen verbs from an unlabeled corpus. Our method re- liesonthehypothesisthatunknownlexical items will be structurally and semantically similar to known items for which annota- tions are available. Accordingly, we rep- resent known and unknown sentences as graphs, formalize the search for the most similar verb as a graph alignment prob- lem and solve the optimization using inte- ger linear programming. Experimental re- sults show that role labeling performance for unknown lexical items improves with training data produced automatically by our method.