Satellite Workshop On Language, Artificial Intelligence and Computer Science for Natural Language Processing Applications (LAICS-NLP): Discovery of Meaning from Text - Ong, Siou Chin.; Kulathuramaiyer, Narayanan; Yeo, Alvin Wee (Faculty of Engineering Kasetsart University, Bangkok, Thailand., 2006)
This paper proposes a novel method to disambiguate important words from a collection of documents. The hypothesis that underlies this approach is that there is a minimal set of senses that are significant in characterizing a context. We extend Yarowsky’s one sense per discourse [13] further to a collection of related documents rather than a single document. We perform distributed clustering on a set of features representing each of the top ten categories of documents in the Reuters-21578 dataset. Groups of terms that have a similar term distributional pattern across documents were identified. WordNet-based similarity measurement was then computed for terms within each cluster. An aggregation of the associations in WordNet that was employed to ascertain term similarity within clusters has provided a means of identifying clusters’ root senses.