George Tsatsaronis, Michalis Vazirgiannis, Ion Androutsopoulos
Most word sense disambiguation (WSD) methods require large quantities of manually annotated training data and/or do not exploit fully the semantic relations of thesauri. We propose a new unsupervised WSD algorithm, which is based on generating Spreading Activation Networks (SANs) from the senses of a thesaurus and the relations between them. A new method of assigning weights to the networks' links is also proposed. Experiments show that the algorithm outperforms previous unsupervised approaches to WSD.
Subjects: 13. Natural Language Processingn
Submitted: Oct 11, 2006