Name
Abstract | ||
|---|---|---|
There is a widely held belief in the natural lan- guage and computational linguistics commu- nities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and in- formation extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich mor- phological features of the language. The ex- periments on the pilot Arabic Propbank data shows that our system based on Support Vec- tor Machines and Kernel Methods yields a global SRL F1 score of 82.17%, which im- proves the current state-of-the-art in Arabic SRL. |
Year | Venue | Keywords |
|---|---|---|
2008 | ACL | semantic role labeling,kernel method,question answering |
Field | DocType | Volume |
Question answering,Computer science,Computational linguistics,PropBank,Information extraction,Natural language,Modern Standard Arabic,Natural language processing,Artificial intelligence,Kernel method,Semantic role labeling,Machine learning | Conference | P08-1 |
Citations | PageRank | References |
8 | 0.51 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mona Diab | 1 | 1945 | 136.84 |
| Alessandro Moschitti | 2 | 3262 | 177.68 |
| daniele pighin | 3 | 289 | 18.72 |