Abstract
Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. We present a principled approach to semantic entailment that builds on inducing re-representations of text snippets into a hierarchical knowledge representation along with an optimization-based inferential mechanism that makes use of it to prove semantic entailment. This paper provides details and analysis of the knowledge representation and knowledge resources issues encountered. We analyze our system’s behavior on the PASCAL text collection and the PARC collection of question-answer pairs. This is used to motivate and explain some of the design decisions in our hierarchical knowledge representation, that is centered around a predicate-argument type abstract representation of text.
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de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D., Sammons, M. (2006). An Inference Model for Semantic Entailment in Natural Language. 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_15
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DOI: https://doi.org/10.1007/11736790_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
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