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UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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This paper describes the UNIBA team participation in the Cross-Level Semantic Similarity task at SemEval 2014. We propose to combine the output of different semantic similarity measures which exploit Word Sense Disambiguation and Distributional Semantic Models, among other lexical features. The integration of similarity measures is performed by means of two supervised methods based on Gaussian Process and Support Vector Machine. Our systems obtained very encouraging results, with the best one
doi:10.3115/v1/s14-2133
dblp:conf/semeval/BasileCS14
fatcat:5r5bqc3ktzcgjcpj42bxbws4ym