This paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by using definitions from English dictionaries. Because the definitions in English dictionaries are not prepared to be transformed into inference rules, some preprocessing steps were taken to turn each relation of word:definition in dictionaries into an inference rule in the form left-hand side ⇒ right-hand side. In this paper, we applied this mechanism using two dictionaries: The MacMillan Dictionary and WordNet definitions. A random set of 200 inference rules were extracted equally from the two dictionaries, and then we used human judgment as to whether these rules are ‘True’ or not. For the MacMillan Dictionary the precision reaches 0.74 with 0.508 recall, and the WordNet definitions resulted in 0.73 precision with 0.09 recall.

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.