UTDMet: Combining WordNet and Corpus Data for Argument Coercion Detection

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.