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Authors: Italo Lopes Oliveira 1 ; Diego Moussallem 2 ; Luís Paulo Faina Garcia 3 and Renato Fileto 1

Affiliations: 1 Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil ; 2 Data Science Group, University of Paderborn, North Rhine-Westphalia, Germany ; 3 Computer Science Department, University of Brasilia, Brasília, Distrito Federal, Brazil

Keyword(s): Entity Linking, Knowledge Embedding, Word Embedding, Deep Neural Network.

Abstract: Entity Linking (EL) for microblog posts is still a challenge because of their usually informal language and limited textual context. Most current EL approaches for microblog posts expand each post context by considering related posts, user interest information, spatial data, and temporal data. Thus, these approaches can be too invasive, compromising user privacy. It hinders data sharing and experimental reproducibility. Moreover, most of these approaches employ graph-based methods instead of state-of-the-art embedding-based ones. This paper proposes a knowledge-intensive EL approach for microblog posts called OPTIC. It relies on a jointly trained word and knowledge embeddings to represent contexts given by the semantics of words and entity candidates for mentions found in the posts. These embedded semantic contexts feed a deep neural network that exploits semantic coherence along with the popularity of the entity candidates for doing their disambiguation. Experiments using the benchm ark system GERBIL shows that OPTIC outperforms most of the approaches on the NEEL challenge 2016 dataset. (More)

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Paper citation in several formats:
Oliveira, I.; Moussallem, D.; Garcia, L. and Fileto, R. (2020). OPTIC: A Deep Neural Network Approach for Entity Linking using Word and Knowledge Embeddings. In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-423-7; ISSN 2184-4992, SciTePress, pages 315-326. DOI: 10.5220/0009351203150326

@conference{iceis20,
author={Italo Lopes Oliveira. and Diego Moussallem. and Luís Paulo Faina Garcia. and Renato Fileto.},
title={OPTIC: A Deep Neural Network Approach for Entity Linking using Word and Knowledge Embeddings},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2020},
pages={315-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009351203150326},
isbn={978-989-758-423-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - OPTIC: A Deep Neural Network Approach for Entity Linking using Word and Knowledge Embeddings
SN - 978-989-758-423-7
IS - 2184-4992
AU - Oliveira, I.
AU - Moussallem, D.
AU - Garcia, L.
AU - Fileto, R.
PY - 2020
SP - 315
EP - 326
DO - 10.5220/0009351203150326
PB - SciTePress