@inproceedings{martins-etal-2019-joint,
title = "Joint Learning of Named Entity Recognition and Entity Linking",
author = "Martins, Pedro Henrique and
Marinho, Zita and
Martins, Andr{\'e} F. T.",
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/P19-2026",
doi = "10.18653/v1/P19-2026",
pages = "190--196",
abstract = "Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach. We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.",
}
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<abstract>Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach. We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.</abstract>
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%0 Conference Proceedings
%T Joint Learning of Named Entity Recognition and Entity Linking
%A Martins, Pedro Henrique
%A Marinho, Zita
%A Martins, André F. T.
%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 martins-etal-2019-joint
%X Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach. We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.
%R 10.18653/v1/P19-2026
%U https://aclanthology.org/P19-2026
%U https://doi.org/10.18653/v1/P19-2026
%P 190-196
Markdown (Informal)
[Joint Learning of Named Entity Recognition and Entity Linking](https://aclanthology.org/P19-2026) (Martins et al., ACL 2019)
ACL
- Pedro Henrique Martins, Zita Marinho, and André F. T. Martins. 2019. Joint Learning of Named Entity Recognition and Entity Linking. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 190–196, Florence, Italy. Association for Computational Linguistics.