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Experimenting a "General Purpose" Textual Entailment Learner in AVE
[chapter]
2007
Lecture Notes in Computer Science
In this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned.
doi:10.1007/978-3-540-74999-8_61
fatcat:swyhrfm5ljd33buh5fs7yab5bm