@inproceedings{tedeschi-etal-2021-named-entity,
title = "{N}amed {E}ntity {R}ecognition for {E}ntity {L}inking: {W}hat Works and What{'}s Next",
author = "Tedeschi, Simone and
Conia, Simone and
Cecconi, Francesco and
Navigli, Roberto",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.220",
doi = "10.18653/v1/2021.findings-emnlp.220",
pages = "2584--2596",
abstract = "Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data {--} millions of labeled examples {--} to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software {--} code and model checkpoints {--} at \url{https://github.com/Babelscape/ner4el}.",
}
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<abstract>Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data – millions of labeled examples – to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software – code and model checkpoints – at https://github.com/Babelscape/ner4el.</abstract>
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%0 Conference Proceedings
%T Named Entity Recognition for Entity Linking: What Works and What’s Next
%A Tedeschi, Simone
%A Conia, Simone
%A Cecconi, Francesco
%A Navigli, Roberto
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F tedeschi-etal-2021-named-entity
%X Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models. However, such systems still require massive amounts of data – millions of labeled examples – to perform at their best, with training times that often exceed several days, especially when limited computational resources are available. In this paper, we look at how Named Entity Recognition (NER) can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. More specifically, we show how and to what extent an EL system can benefit from NER to enhance its entity representations, improve candidate selection, select more effective negative samples and enforce hard and soft constraints on its output entities. We release our software – code and model checkpoints – at https://github.com/Babelscape/ner4el.
%R 10.18653/v1/2021.findings-emnlp.220
%U https://aclanthology.org/2021.findings-emnlp.220
%U https://doi.org/10.18653/v1/2021.findings-emnlp.220
%P 2584-2596
Markdown (Informal)
[Named Entity Recognition for Entity Linking: What Works and What’s Next](https://aclanthology.org/2021.findings-emnlp.220) (Tedeschi et al., Findings 2021)
ACL