@inproceedings{zaratiana-etal-2022-named,
title = "Named Entity Recognition as Structured Span Prediction",
author = "Zaratiana, Urchade and
Tomeh, Nadi and
Holat, Pierre 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-1.1",
doi = "10.18653/v1/2022.umios-1.1",
pages = "1--10",
abstract = "Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. While the dominant paradigm of NER is sequence labelling, span-based approaches have become very popular in recent times but are less well understood. In this work, we study different aspects of span-based NER, namely the span representation, learning strategy, and decoding algorithms to avoid span overlap. We also propose an exact algorithm that efficiently finds the set of non-overlapping spans that maximizes a global score, given a list of candidate spans. We performed our study on three benchmark NER datasets from different domains. We make our code publicly available at \url{https://github.com/urchade/span-structured-prediction}.",
}
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%0 Conference Proceedings
%T Named Entity Recognition as Structured Span Prediction
%A Zaratiana, Urchade
%A Tomeh, Nadi
%A Holat, Pierre
%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-named
%X Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. While the dominant paradigm of NER is sequence labelling, span-based approaches have become very popular in recent times but are less well understood. In this work, we study different aspects of span-based NER, namely the span representation, learning strategy, and decoding algorithms to avoid span overlap. We also propose an exact algorithm that efficiently finds the set of non-overlapping spans that maximizes a global score, given a list of candidate spans. We performed our study on three benchmark NER datasets from different domains. We make our code publicly available at https://github.com/urchade/span-structured-prediction.
%R 10.18653/v1/2022.umios-1.1
%U https://aclanthology.org/2022.umios-1.1
%U https://doi.org/10.18653/v1/2022.umios-1.1
%P 1-10
Markdown (Informal)
[Named Entity Recognition as Structured Span Prediction](https://aclanthology.org/2022.umios-1.1) (Zaratiana et al., UM-IoS 2022)
ACL
- Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. 2022. Named Entity Recognition as Structured Span Prediction. In Proceedings of the Workshop on Unimodal and Multimodal Induction of Linguistic Structures (UM-IoS), pages 1–10, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.