This paper presents an algorithm to apply the smoothing techniques described in [1] to three different Machine Learning (ML) methods for Word Sense Disambiguation (WSD). The method...
: This paper presents an automatic method and interface to enrich semantically WordNet with categories from general domain classification systems. The method is performed in two co...
Abstract. This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar–basedclassification, on the Word Sens...
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...
Abstract. We report on the design of a system for correcting spelling errors resulting in non-existent words. The system aims at improving edition of medical reports. Unlike tradit...