Name
Abstract | ||
|---|---|---|
In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%. |
Year | Venue | Keywords |
|---|---|---|
2005 | CoNLL | one-versus-all adaboost classifier,best system,partial syntax,rich set,simple approach,full syntactic analysis,semantic argument map,complete syntactic analysis,semantic attribute,syntactic phrase,semantic role |
Field | DocType | Citations |
AdaBoost,Syntactic predicate,Computer science,Phrase,Artificial intelligence,Natural language processing,Parsing,Argument map,Syntax,Semantic computing,Semantic role labeling | Conference | 31 |
PageRank | References | Authors |
1.65 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mihai Surdeanu | 1 | 2582 | 174.69 |
| Jordi Turmo | 2 | 306 | 30.52 |