Paper: UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity

ACL ID S14-2133
Title UNIBA: Combining Distributional Semantic Models and Word Sense Disambiguation for Textual Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes the UNIBA team participation in the Cross-Level Semantic Similarity task at SemEval 2014. We pro- pose to combine the output of different se- mantic similarity measures which exploit Word Sense Disambiguation and Distribu- tional Semantic Models, among other lex- ical features. The integration of similar- ity measures is performed by means of two supervised methods based on Gaus- sian Process and Support Vector Machine. Our systems obtained very encouraging results, with the best one ranked 6 th out of 38 submitted systems.