Title: Machine learning with semantic-based distances between sentences for textual entailment
Abstract: This paper describes our experiments on Textual Entailment in the context of the Third Pascal Recognising Textual Entailment (RTE-3) Evaluation Challenge. Our system uses a Machine Learning approach with Support Vector Machines and AdaBoost to deal with the RTE challenge. We perform a lexical, syntactic, and semantic analysis of the entailment pairs. From this information we compute a set of semantic-based distances between sentences. The results look promising specially for the QA entailment task.