An Inference Model for Semantic Entailment in Natural Language

Semantic entailment is the problem of determining if the meaning of a given sentence entails that of another. This is a fundamental problem in natural language understanding that provides a broad framework for studying language variability and has a large number of applications. This paper presents a principled approach to this problem that builds on inducing representations of text snippets into a hierarchical knowledge representation along with a sound optimization-based inferential mechanism that makes use of it to decide semantic entailment. A preliminary evaluation on the PASCAL text collection is presented.

• Type: Conference
• Year: 2005
• Where: MLCW
• Authors: Rodrigo de Salvo Braz, Roxana Girju, Vasin Punyakanok, Dan Roth, Mark Sammons
• Keywords: Machine Learning, MLCW 2005, Natural Language Understanding, Optimization-based Inferential Mechanism, Semantic Entailment