On-demand injection of lexical knowledge for recognising textual entailment

Pascual Martínez-Gómez, Koji Mineshima, Yusuke Miyao, Daisuke Bekki

研究成果: Conference contribution

7 引用 (Scopus)

抜粋

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 logicbased systems and is competitive with state-of-the-art statistical methods.

元の言語English
ホスト出版物のタイトルLong Papers - Continued
出版者Association for Computational Linguistics (ACL)
ページ710-720
ページ数11
ISBN(電子版)9781510838604
出版物ステータスPublished - 2017 1 1
外部発表Yes
イベント15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Valencia, Spain
継続期間: 2017 4 32017 4 7

出版物シリーズ

名前15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference
1

Conference

Conference15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017
Spain
Valencia
期間17/4/317/4/7

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics

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  • これを引用

    Martínez-Gómez, P., Mineshima, K., Miyao, Y., & Bekki, D. (2017). On-demand injection of lexical knowledge for recognising textual entailment. : Long Papers - Continued (pp. 710-720). (15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference; 巻数 1). Association for Computational Linguistics (ACL).