On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
Pascual
Martínez-Gómez
author
Koji
Mineshima
author
Yusuke
Miyao
author
Daisuke
Bekki
author
2017-04
text
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Mirella
Lapata
editor
Phil
Blunsom
editor
Alexander
Koller
editor
Association for Computational Linguistics
Valencia, Spain
conference publication
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.
martinez-gomez-etal-2017-demand
https://aclanthology.org/E17-1067
2017-04
710
720