Paper: Semantic Dependency Parsing of NomBank and PropBank: An Efficient Integrated Approach via a Large-scale Feature Selection

ACL ID D09-1004
Title Semantic Dependency Parsing of NomBank and PropBank: An Efficient Integrated Approach via a Large-scale Feature Selection
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

We present an integrated dependency- based semantic role labeling system for English from both NomBank and Prop- Bank. By introducing assistant argument labels and considering much more fea- ture templates, two optimal feature tem- plate sets are obtained through an effec- tive feature selection procedure and help construct a high performance single SRL system. From the evaluations on the date set of CoNLL-2008 shared task, the per- formance of our system is quite close to the state of the art. As to our knowl- edge, this is the first integrated SRL sys- tem that achieves a competitive perfor- mance against previous pipeline systems.