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        <title>On-demand Injection of Lexical Knowledge for Recognising Textual Entailment</title>
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        <namePart type="given">Pascual</namePart>
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        <namePart type="given">Koji</namePart>
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        <namePart type="given">Yusuke</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T On-demand Injection of Lexical Knowledge for Recognising Textual Entailment
%A Martínez-Gómez, Pascual
%A Mineshima, Koji
%A Miyao, Yusuke
%A Bekki, Daisuke
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F martinez-gomez-etal-2017-demand
%X 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.
%U https://aclanthology.org/E17-1067
%P 710-720

Markdown (Informal)

[On-demand Injection of Lexical Knowledge for Recognising Textual Entailment](https://aclanthology.org/E17-1067) (Martínez-Gómez et al., EACL 2017)

• On-demand Injection of Lexical Knowledge for Recognising Textual Entailment (Martínez-Gómez et al., EACL 2017)

ACL

• Pascual Martínez-Gómez, Koji Mineshima, Yusuke Miyao, and Daisuke Bekki. 2017. On-demand Injection of Lexical Knowledge for Recognising Textual Entailment. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 710–720, Valencia, Spain. Association for Computational Linguistics.