Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction

Ian Wood, Mark Johnson, Stephen Wan


Abstract
Relation prediction informed from a combination of text corpora and curated knowledge bases, combining knowledge graph completion with relation extraction, is a relatively little studied task. A system that can perform this task has the ability to extend an arbitrary set of relational database tables with information extracted from a document corpus. OpenKi[1] addresses this task through extraction of named entities and predicates via OpenIE tools then learning relation embeddings from the resulting entity-relation graph for relation prediction, outperforming previous approaches. We present an extension of OpenKi that incorporates embeddings of text-based representations of the entities and the relations. We demonstrate that this results in a substantial performance increase over a system without this information.
Anthology ID:
2021.naacl-main.268
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3429–3436
Language:
URL:
https://aclanthology.org/2021.naacl-main.268
DOI:
10.18653/v1/2021.naacl-main.268
Bibkey:
Cite (ACL):
Ian Wood, Mark Johnson, and Stephen Wan. 2021. Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3429–3436, Online. Association for Computational Linguistics.
Cite (Informal):
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction (Wood et al., NAACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2021.naacl-main.268.pdf
Video:
 https://preview.aclanthology.org/dois-2013-emnlp/2021.naacl-main.268.mp4
Code
 drevicko/openki