This paper explores the large-scale acquisition of sense-tagged examples for Word Sense Disambiguation (WSD). We have applied the "WordNet monosemous relatives" method t...
Abstract— In this paper we suggest a new approach to represent text document collections, integrating background knowledge to improve clustering effectiveness. Background knowled...
We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...
Background: Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be u...
Mihalcea [1] discusses self-training and co-training in the context of word sense disambiguation and shows that parameter optimization on individual words was important to obtain g...