Acquiring knowledge from the web to be used as selectors for noun sense disambiguation

Abstract

This paper presents a method of acquiring knowledge from the Web for noun sense disambiguation. Words, called selectors, are acquired which take the place of an instance of a target word in its local context. The selectors serve for the system to essentially learn the areas or concepts of WordNet that the sense of a target word should be a part of. The correct sense is chosen based on a combination of the strength given from similarity and relatedness measures overWordNet and the probability of a selector occurring within the local context. Our method is evaluated using the coarse-grained all-words task from SemEval 2007. Experiments reveal that pathbased similarity measures perform just as well as information content similarity measures within our system. Overall, the results show our system is out-performed only by systems utilizing training data or substantially more annotated data. © 2008.