@inproceedings{sevgili-etal-2019-improving,
title = "Improving Neural Entity Disambiguation with Graph Embeddings",
author = {Sevgili, {\"O}zge and
Panchenko, Alexander and
Biemann, Chris},
editor = "Alva-Manchego, Fernando and
Choi, Eunsol and
Khashabi, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-2044",
doi = "10.18653/v1/P19-2044",
pages = "315--322",
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.",
}
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%0 Conference Proceedings
%T Improving Neural Entity Disambiguation with Graph Embeddings
%A Sevgili, Özge
%A Panchenko, Alexander
%A Biemann, Chris
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F sevgili-etal-2019-improving
%X 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.
%R 10.18653/v1/P19-2044
%U https://aclanthology.org/P19-2044
%U https://doi.org/10.18653/v1/P19-2044
%P 315-322
Markdown (Informal)
[Improving Neural Entity Disambiguation with Graph Embeddings](https://aclanthology.org/P19-2044) (Sevgili et al., ACL 2019)
ACL
- Özge Sevgili, Alexander Panchenko, and Chris Biemann. 2019. Improving Neural Entity Disambiguation with Graph Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 315–322, Florence, Italy. Association for Computational Linguistics.