GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks

Urchade Zaratiana, Nadi Tomeh, Pierre Holat, Thierry Charnois


Abstract
There are two main paradigms for Named Entity Recognition (NER): sequence labelling and span classification. Sequence labelling aims to assign a label to each word in an input text using, for example, BIO (Begin, Inside and Outside) tagging, while span classification involves enumerating all possible spans in a text and classifying them into their labels. In contrast to sequence labelling, unconstrained span-based methods tend to assign entity labels to overlapping spans, which is generally undesirable, especially for NER tasks without nested entities. Accordingly, we propose GNNer, a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction. Our approach reduces the number of overlapping spans compared to strong baseline while maintaining competitive metric performance. Code is available at https://github.com/urchade/GNNer.
Anthology ID:
2022.acl-srw.9
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Samuel Louvan, Andrea Madotto, Brielen Madureira
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
97–103
Language:
URL:
https://aclanthology.org/2022.acl-srw.9
DOI:
10.18653/v1/2022.acl-srw.9
Bibkey:
Cite (ACL):
Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. 2022. GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 97–103, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks (Zaratiana et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-srw.9.pdf
Video:
 https://aclanthology.org/2022.acl-srw.9.mp4
Code
 urchade/gnner
Data
CoNLL 2003SciERC