Paper: Semi-Supervised Semantic Role Labeling

ACL ID E09-1026
Title Semi-Supervised Semantic Role Labeling
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

Large scale annotated corpora are pre- requisite to developing high-performance semantic role labeling systems. Unfor- tunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creat- ing resources for semantic role labeling via semi-supervised learning. Our algo- rithm augments a small number of man- ually labeled instances with unlabeled ex- amples whose roles are inferred automat- ically via annotation projection. We for- mulate the projection task as a generaliza- tion of the linear assignment problem. We seek to find a role assignment in the un- labeled data such that the argument sim- ilarity between the labeled and unlabeled instances is maximized. Experimental re- sults on semantic role...