@InProceedings{martinezgomez-EtAl:2017:EACLlong,
  author    = {Mart\'{i}nez-G\'{o}mez, Pascual  and  Mineshima, Koji  and  Miyao, Yusuke  and  Bekki, Daisuke},
  title     = {On-demand Injection of Lexical Knowledge for Recognising Textual Entailment},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {710--720},
  abstract  = {We approach the recognition of textual entailment using logical semantic
	representations and a theorem prover.  In this setup, lexical divergences that
	preserve semantic entailment between the source and target texts need to be
	explicitly stated.  However, recognising subsentential semantic relations is
	not trivial.  We address this problem by monitoring the proof of the theorem
	and detecting unprovable sub-goals that share predicate arguments with logical
	premises. If a linguistic relation exists, then an appropriate axiom is
	constructed on-demand and the theorem proving continues.  Experiments show that
	this approach is effective and precise, producing a system that outperforms
	other logic-based systems and is competitive with state-of-the-art statistical
	methods.},
  url       = {http://www.aclweb.org/anthology/E17-1067}
}