Name
Abstract | ||
|---|---|---|
Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be applied. In this paper, we used syntactic subtrees that span potential argument structures of the target predicate in tree kernel functions. This allows Support Vector Machines to discern between correct and incorrect predicate structures and to re-rank them based on the joint probability of their arguments. Experiments on the PropBank data show that both classification and re-ranking based on tree kernels can improve SRL systems. |
Year | Venue | Keywords |
|---|---|---|
2006 | CoNLL | tree kernel joint inference,whole predicate argument structure,srl system,joint probability,target predicate,tree kernel,joint inference,incorrect predicate structure,propbank data,potential argument structure,tree kernel function,semantic role,semantic role labeling |
Field | DocType | Citations |
Joint probability distribution,Inference,Computer science,Support vector machine,PropBank,Tree kernel,Natural language processing,Artificial intelligence,Predicate (grammar),Syntax,Semantic role labeling,Machine learning | Conference | 34 |
PageRank | References | Authors |
1.24 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Moschitti | 1 | 3262 | 177.68 |
| daniele pighin | 2 | 289 | 18.72 |
| Roberto Basili | 3 | 1308 | 155.68 |