UCD-FC: Deducing semantic relations using WordNet senses that occur frequently in a database of noun-noun compounds

This paper describes a system for classifying semantic relations among nominals, as in SemEval task 4. This system uses a corpus of 2,500 compounds annotated with WordNet senses and covering 139 different semantic relations. Given a set of nominal pairs for training, as provided in the SemEval task 4 training data, this system constructs for each training pair a set of features made up of relations and WordNet sense pairs which occurred with those nominals in the corpus. A Naive Bayes learning algorithm learns associations between these features and relation membership categories. The identification of relations among nominals in test items takes place on the basis of