Local Vector-based Models for Sense Discrimination
Marie-Catherine de Marneffe, Cédric Archambeau, Pierre Dupont, Michel Verleysen · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2005
Word sense discrimination aims at automatically determining which instances of an ambiguous word share the same sense. A fully unsupervised technique based on a high dimensional vector representation of word senses was proposed by Sch¨utze [10]. While this model was assumed to be Gaussian, results were only reported for the K-means approximation. In this work, a local vector-based model of reduced dimensionality which is linguistically coherent and can be computed for multivariate Gaussian mixtures is proposed. Several practical experiments are conducted on the New York Times News 1997 corpus. They show the advantages of unrestricted Gaussian models compared to K-means. The correct discrimination rate is further increased when using regularized Gaussian models as proposed in [2].