A Machine Learning Approach for Recognizing Textual Entailment in Spanish

This paper presents a system that uses machine learning algorithms for the task of recognizing textual entailment in Spanish language. The datasets used include SPARTE Corpus and a translated version to Spanish of RTE3, RTE4 and RTE5 datasets. The features chosen quantify lexical, syntactic and semantic level matching between text and hypothesis sentences. We analyze how the different sizes of datasets and classifiers could impact on the final overall performance of the RTE classification of two-way task in Spanish. The RTE system yields 60.83% of accuracy and a competitive result of 66.50% of accuracy is reported by train and test set taken from SPARTE Corpus with 70% split.