@inproceedings{zaratiana-etal-2022-global,
title = "Global Span Selection for Named Entity Recognition",
author = "Zaratiana, Urchade and
El Elkhbir, Niama and
Holat, Pierre and
Tomeh, Nadi and
Charnois, Thierry",
editor = "Han, Wenjuan and
Zheng, Zilong and
Lin, Zhouhan and
Jin, Lifeng and
Shen, Yikang and
Kim, Yoon and
Tu, Kewei",
booktitle = "Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.umios-1.2",
doi = "10.18653/v1/2022.umios-1.2",
pages = "11--17",
abstract = "Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.",
}
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%0 Conference Proceedings
%T Global Span Selection for Named Entity Recognition
%A Zaratiana, Urchade
%A El Elkhbir, Niama
%A Holat, Pierre
%A Tomeh, Nadi
%A Charnois, Thierry
%Y Han, Wenjuan
%Y Zheng, Zilong
%Y Lin, Zhouhan
%Y Jin, Lifeng
%Y Shen, Yikang
%Y Kim, Yoon
%Y Tu, Kewei
%S Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F zaratiana-etal-2022-global
%X Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.
%R 10.18653/v1/2022.umios-1.2
%U https://aclanthology.org/2022.umios-1.2
%U https://doi.org/10.18653/v1/2022.umios-1.2
%P 11-17
Markdown (Informal)
[Global Span Selection for Named Entity Recognition](https://aclanthology.org/2022.umios-1.2) (Zaratiana et al., UM-IoS 2022)
ACL
- Urchade Zaratiana, Niama El Elkhbir, Pierre Holat, Nadi Tomeh, and Thierry Charnois. 2022. Global Span Selection for Named Entity Recognition. In Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS), pages 11–17, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.