Learning noun-modifier semantic relations with corpus-based and WordNet-based features

Abstract

We study the performance of two representations of word meaning in learning noun-modifier semantic relations. One representation is based on lexical resources, in particular WordNet, the other - on a corpus. We experimented with decision trees, instance-based learning and Support Vector Machines. All these methods work well in this learning task. We report high precision, recall and F-score, and small variation in performance across several 10-fold cross-validation runs. The corpus-based method has the advantage of working with data without word-sense annotations and performs well over the baseline. The WordNet-based method, requiring word-sense annotated data, has higher precision.