ELDEN: Improved entity linking using densified knowledge graphs

Radhakrishnan, PP and Talukdar, PK and Varma, V (2018) ELDEN: Improved entity linking using densified knowledge graphs. In: 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1 Jun 6 2018, New Orleans, pp. 1844-1853.

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Abstract

Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system's ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms stateof-the-Art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN's approach is simple, yet effective. We have made ELDEN's code and data publicly available. © 2018 The Association for Computational Linguistics.

Item Type:	Conference Paper
Publication:	NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Publisher:	Association for Computational Linguistics (ACL)
Additional Information:	The copyright of this article belongs to Association for Computational Linguistics (ACL)
Keywords:	Arts computing; Embeddings, Benchmark datasets; Co-occurrence statistics; Degree of connectivity; Entity similarities; Knowledge graphs; State of the art; Text corpora, Computational linguistics
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	31 Mar 2021 10:59
Last Modified:	31 Mar 2021 10:59
URI:	http://eprints.iisc.ac.in/id/eprint/65334

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