Duality of link prediction and entailment graph induction

Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

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Abstract

Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
Original languageEnglish
Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics
EditorsAnna Korhonen, David Traum, Lluís Màrquez
Place of PublicationFlorence, Italy
PublisherAssociation for Computational Linguistics
Pages4736-4746
Number of pages11
ISBN (Print)9781950737482
DOIs
Publication statusPublished - Jul 2019
EventAnnual Meeting of the Association for Computational Linguistics (57th : 2019) - Florence, Italy
Duration: 28 Jul 20192 Aug 2019

Conference

ConferenceAnnual Meeting of the Association for Computational Linguistics (57th : 2019)
CountryItaly
CityFlorence
Period28/07/192/08/19

Bibliographical note

Copyright the Publisher 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Cite this

Hosseini, M. J., Cohen, S. B., Johnson, M., & Steedman, M. (2019). Duality of link prediction and entailment graph induction. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4736-4746). Florence, Italy: Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1468