Improving Neural Entity Disambiguation with Graph Embeddings

Özge Sevgili, Alexander Panchenko, Chris Biemann


Abstract
Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities. In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations. Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-of-the-art neural ED system.
Anthology ID:
P19-2044
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
315–322
Language:
URL:
https://www.aclweb.org/anthology/P19-2044
DOI:
10.18653/v1/P19-2044
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-2044.pdf