A graph-Based Approach to WSD Using Relevant Semantic Trees and N-Cliques Model

In this paper we propose a new graph-based approach to solve semantic ambiguity using a semantic net based on WordNet. Our proposal uses an adaptation of the Clique Partitioning Technique to extract sets of strongly related senses. For that, an initial graph is obtained from senses of WordNet combined with the information of several semantic categories from different resources: WordNet Domains, SUMO and WordNet Affect. In order to obtain the most relevant concepts in a sentence we use the Relevant Semantic Trees method. The evaluation of the results has been conducted using the test data set of Senseval-2 obtaining promising results.

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