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This paper presents the development of a natural language inference engine that benefits from two current standard approaches; i.e., shallow and deep approaches. This system combines two non-deterministic algorithms: the approximate matching from the shallow approach and a theorem prover from the deep approach for handling multi-step inference tasks. The theorem prover is customized to accept dependency trees and apply inference rules to these trees. The inference rules are automatically generated as syllogistic rules from our test data (FraCaS test suite). The theorem prover exploits a non-deterministic matching algorithm within a standard backward chaining inference engine. We employ continuation programming as a way of seamlessly handling the combination of these two non-deterministic algorithms. Testing the matching algorithm on “Generalized quantifiers” and “adjectives” topics in FraCaS (MacCartney and Manning 2007), we achieved an accuracy of 92.8% of the single-premise cases. For the multi-steps of inference, we checked the validity of our syllogistic rules and then extracted four generic instances that can be applied to more than one problem.

A Hybrid System to apply Natural Language Inference over Dependency Trees