Experimenting a "General Purpose" Textual Entailment Learner in AVE

In this paper we present the use of a ”general purpose” textual entaiment recognizer in the Answer Validation Exercise (AVE) task. Our system has been developed to learn entailment rules from annotated examples. The main idea of the system is the cross-pair similirity measure we defined. This similarity allows us to define an implicit feature space using kernel functions in SVM learners. We experimented with our system using different training and testing sets: RTE data sets and AVE data sets. The comparative results show that entailment rules can be learned from data sets, e.g. RTE, that are different from AVE. Moreover, it seems that better results are obtained using more controlled training data (the RTE set) that less controlled ones (the AVE development set). Although, the high variability of the outcome prevents us to derive definitive conclusions, the results of our system show that our approach is quite promising and improvable in the future.