Discovering Implicit Knowledge with Unary Relations

Michael Glass, Alfio Gliozzo


Abstract
State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence. However, the vast majority of relations are either implicit or not sententially localized. This is a major problem for Knowledge Base Population, severely limiting recall. In this paper we propose a new methodology to identify relations between two entities, consisting of detecting a very large number of unary relations, and using them to infer missing entities. We describe a deep learning architecture able to learn thousands of such relations very efficiently by using a common deep learning based representation. Our approach largely outperforms state of the art relation extraction technology on a newly introduced web scale knowledge base population benchmark, that we release to the research community.
Anthology ID:
P18-1147
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1585–1594
Language:
URL:
https://aclanthology.org/P18-1147
DOI:
10.18653/v1/P18-1147
Bibkey:
Cite (ACL):
Michael Glass and Alfio Gliozzo. 2018. Discovering Implicit Knowledge with Unary Relations. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1585–1594, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Discovering Implicit Knowledge with Unary Relations (Glass & Gliozzo, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1147.pdf
Poster:
 P18-1147.Poster.pdf
Code
 IBM/cc-dbp
Data
DBpedia