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The NatLog Project PageOverviewThe NatLog project aims to develop an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. We have greatly extended past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. The NatLog system decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. Using the NatLog system, we've achieved 70% accuracy and 89% precision on the FraCaS test suite. We've also shown that adding NatLog as a component in the Stanford RTE system leads to accuracy gains of 4%. PeoplePapers
Modeling semantic containment and exclusion in natural language inference
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A phrase-based alignment model for natural language inference
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Natural logic for textual inference
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Learning Alignments and Leveraging Natural Logic
[pdf] Related Work
Linguistics and Natural Logic
[pdf] Resources
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