Name
Abstract | ||
|---|---|---|
This paper describes our system for the classification of argument coercion for SemEval-2010 Task 7. We present two approaches to classifying an argument's semantic class, which is then compared to the predicate's expected semantic class to detect coercions. The first approach is based on learning the members of an arbitrary semantic class using WordNet's hypernymy structure. The second approach leverages automatically extracted semantic parse information from a large corpus to identify similar arguments by the predicates that select them. We show the results these approaches obtain on the task as well as how they can improve a traditional feature-based approach. |
Year | Venue | Keywords |
|---|---|---|
2010 | SemEval@ACL | combining wordnet,similar argument,argument coercion detection,arbitrary semantic class,hypernymy structure,corpus data,semantic class,argument coercion,large corpus,traditional feature-based approach,semantic parse information,semeval-2010 task |
Field | DocType | Citations |
Computer science,Natural language processing,Artificial intelligence,Parsing,Predicate (grammar),WordNet | Conference | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kirk Roberts | 1 | 334 | 39.86 |
| Sanda Harabagiu | 2 | 2203 | 221.65 |