Semantic Classes and Relevant Domains on WSD

Language ambiguities are a problem in various fields. For example, in Machine Translation themajor cause of errors is ambiguity.Moreover, ambiguous words can be confusing for Information Extraction algorithms. Our purpose in this work is to provide a new approach to solve semantic ambiguities by dealing with the problem of the fine granularity of sense inventories. Our goal is to replaceword senses with Semantic Classes that share properties, features and meanings. Also another semantic resources, Relevant Domains, is used to extract extract semantic information and enrich the process. The results obtained are evaluated in the Evaluation Exercises for the Semantic Analysis of Text (SensEval) framework.