Inducing ontological co-occurrence vectors

Abstract

In this paper, we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show 73.9% attachment accuracy in the first position and 81.3% accuracy in the top-5 positions. This framework could potentially serve as a foundation for ontologizing lexical-semantic resources and assist the development of other largescale and internally consistent collections of semantic information. © 2005 Association for Computational Linguistics.