Abstract
This paper describes the system that LCC has devised to perform textual entailment recognition for the PASCAL RTE Challenge. Our system transforms each text-hypothesis pair into a two-layered logic form representation that expresses the lexical, syntactic, and semantic attributes of the text and hypothesis. A large set of natural language axioms are constructed for each text-hypothesis pair that help connect concepts in the hypothesis with concepts in the text. Our natural language logic prover is then used to prove entailment through abductive reasoning. The system’s performance in the challenge resulted in an accuracy of 55%.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hodges, D., Clark, C., Fowler, A., Moldovan, D. (2006). Applying COGEX to Recognize Textual Entailment. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_24
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DOI: https://doi.org/10.1007/11736790_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
Online ISBN: 978-3-540-33428-6
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