Aligning semantic graphs for textual inference and machine reading

This paper presents our work on textual inference and situates it within the context of the larger goals of ma- chine reading. The textual inference task is to deter- mine if the meaning of one text can be inferred from the meaning of another and from background knowledge. Our system generates semantic graphs as a representa- tion of the meaning of a text. This paper presents new results for aligning pairs of semantic graphs, and pro- poses the application of natural logic to derive infer- ence decisions from those aligned pairs. We consider this work as first steps toward a system able to demon- strate broad-coverage text understanding and learning abilities.