Speaker
Affilliation
Rome
Abstract
Word Sense Disambiguation (WSD) is the task of computationally determining the appropriate meaning of words in context. Most approaches to WSD adopt WordNet (Fellbaum, 1998) as a reference sense inventory. Unfortunately, WordNet encodes very fine-grained sense distinctions which make it hard for WSD systems to exceed a 65\% accuracy in an all-words setting. In this talk, we propose two different approaches to this problem. First, we accept to deal with the fineness of the WordNet sense inventory. We propose a method to adjudicate the disagreements between sense annotators based on the exploitation of the lexicon structure as a justification of the final sense choices. The method allows to adjudicate both manual and automatic disagreements and attains 68.5\% accuracy in the validation of the 3 best-ranking systems in the Senseval-3 all-words task. Second, we present an approach to the clustering of WordNet word senses via a mapping to coarser sense distinctions from a machine-readable edition of the Oxford Dictionary of English (ODE). We show that the resulting clustering is reliable and that state-of-the-art systems achieve up to 78\% accuracy when adopting the resulting sense clustering in the Senseval-3 all-words setting.