UCD-PN: classification of semantic relations between nominals using wordnet and web counts

For our system we use the SMO implementation of a support vector machine provided with the WEKA machine learning toolkit. As with all machine learning approaches, the most important step is to choose a set of features which reliably help to predict the label of the example. We used 76 features drawn from two very different knowledge sources. The first 48 features are Boolean values indicating whether or not each of the nominals in the sentence are linked to certain other words in the WordNet hypernym and meronym networks. The remaining 28 features are web frequency counts for the two nominals joined by certain common prepositions and verbs. Our system performed well on all but two of the relations; theme-tool and origin entity.