UMCC_DLSI: Textual Similarity based on Lexical-Semantic features

This paper describes the specifications and results of UMCC_DLSI system, which participated in the Semantic Textual Similarity task (STS) of SemEval-2013. Our supervised system uses different types of lexical and semantic features to train a Bagging classifier used to decide the correct option. Related to the different features we can highlight the resource ISR-WN used to extract semantic relations among words and the use of different algorithms to establish semantic and lexical similarities. In order to establish which features are the most appropriate to improve STS results we participated with three runs using different set of features. Our best run reached the position 44 in the official ranking, obtaining a general correlation coefficient of 0.61.