Learning Noun-Modifier Semantic Relations with Corpus-based and WordNet-based Features

Vivi Nastase, Jelber Sayyad Shirabad, Marina Sokolova, Stan Szpakowicz

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.

Subjects: 13. Natural Language Processing; 12. Machine Learning and Discovery


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