Word Sense Disambiguation with Spreading Activation Networks Generated from Thesauri
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.