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Duality of Link Prediction and Entailment Graph Induction

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https://www.aclweb.org/anthology/P19-1468
Original languageEnglish
Title of host publicationProceedings of the 57th Annual Conference of the Association for Computational Linguistics (long papers)
EditorsAnna Korhonen, David Traum, Lluís Màrquez
Place of PublicationFlorence, Italy
PublisherACL Anthology
Pages4736–4746
Number of pages11
Volume1
ISBN (Print)978-1-950737-48-2
Publication statusE-pub ahead of print - 2 Aug 2019
Event57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy
Duration: 28 Jul 20192 Aug 2019
Conference number: 57
http://www.acl2019.org/EN/index.xhtml

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2019
CountryItaly
CityFlorence
Period28/07/192/08/19
Internet address

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.

Event

57th Annual Meeting of the Association for Computational Linguistics

28/07/192/08/19

Florence, Italy

Event: Conference

ID: 95488244