Robust Graph Alignment Methods for Textual Inference and Machine Reading

Marie-Catherine de Marneffe, Trond Grenager, Bill MacCartney, Daniel Cer, Daniel Ramage, Chloe Kiddon, Christopher D. Manning

This paper presents our work on textual inference and situates it within the context of the larger goals of machine reading. The textual inference task is to determine if the meaning of one text can be inferred from the meaning of another combined with background knowledge. Most existing work either provides only very limited text understanding by using bag-of-words lexical similarity models or suffers from the brittleness typical of complex natural language understanding systems. Our system generates semantic graphs as a representation of the meaning of a text. This paper presents new results for aligning pairs of semantic graphs, and proposes the application of natural logic to derive inference decisions from those aligned pairs. We consider this work as first steps toward a system able to demonstrate broad-coverage text understanding and learning abilities.

Subjects: 13. Natural Language Processing; 12. Machine Learning and Discovery

Submitted: Jan 26, 2007

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