UMCC_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance

This paper describes the specifications and results of UMCC_DLSI-(EPS) system, which participated in the first Evaluating Phrasal Semantics of SemEval-2013. Our supervised system uses different kinds of semantic features to train a bagging classifier used to select the correct similarity option. Related to the different features we can highlight the resource WordNet used to extract semantic relations among words and the use of different algorithms to establish semantic similarities. Our system obtains promising results with a precision value around 78% for the English corpus and 71.84% for the Italian corpus.

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