Most word sense disambiguation (WSD) methods require large quantities of manually annotated training data and/or do not exploit fully the semantic relations of thesauri. We propos...
George Tsatsaronis, Michalis Vazirgiannis, Ion And...
Word sense disambiguation has always been a key problem in Natural Language Processing. In the paper, we use the method of Information Gain to calculate the weight of different po...
Abstract. A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorith...
Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current stateof-the-art methods in taxonomy learning have d...
This paper describes a program that disambignates English word senses in unrestricted text using statistical models of the major Roget's Thesaurus categories. Roget's ca...