Wordnet Wordsense Disambigioution using an Automatically Generated Ontology

In this paper we present a word sense disambiguation method in which ambiguous words are first disambiguated to senses from an automatically generated ontology, and from there mapped to Wordnet senses. We use the ”clustering by committee” algorithm to automatically generate sense clusters given untagged text. The content of each cluster is used to map ambiguous words from those clusters to Wordnet senses. The algorithm does not require any training data, but we suspect that performance could be improved by supplementing the text to be disambiguated with untagged text from a similar source. We compare our algorithm to a similar disambiguation scheme that does not make use of automatically generated senses, as well as too an intermediate algorithm that makes use of the automatically generated semantic categories, but does not limit itself to the actual sense clusters. While what results we were able to gather show that the direct disambiguator outperforms our other two algorithms, there are a number of reasons not to give up hope in the approach.