Supersense tagging of unknown nouns in WordNet

It is shown how information contained in the dictionary can be used as additional training data that improves accuracy in learning new nouns and defines a more realistic evaluation procedure than cross-validation.

Abstract

We present a new framework for classifying common nouns that extends named-entity classification. We used a fixed set of 26 semantic labels, which we called supersenses. These are the labels used by lexicographers developing WordNet. This framework has a number of practical advantages. We show how information contained in the dictionary can be used as additional training data that improves accuracy in learning new nouns. We also define a more realistic evaluation procedure than cross-validation.