@InProceedings{almiman-ramsay:2017:RANLP2,
  author    = {Almiman, Ali  and  Ramsay, Allan},
  title     = {A Hybrid System to apply Natural Language Inference over Dependency Trees},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {64--70},
  abstract  = {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 chain- ing inference engine. We
	employ continuation programming as a way of seamlessly handling the combination
	of these two non-deterministic algorithms. Test- ing 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.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_010}
}