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        <title>GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks</title>
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        <namePart type="given">Urchade</namePart>
        <namePart type="family">Zaratiana</namePart>
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        <namePart type="given">Nadi</namePart>
        <namePart type="family">Tomeh</namePart>
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    <name type="personal">
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    <name type="personal">
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            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Samuel</namePart>
            <namePart type="family">Louvan</namePart>
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        <name type="personal">
            <namePart type="given">Andrea</namePart>
            <namePart type="family">Madotto</namePart>
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            <namePart type="family">Madureira</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.acl-srw.9</identifier>
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%0 Conference Proceedings
%T GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks
%A Zaratiana, Urchade
%A Tomeh, Nadi
%A Holat, Pierre
%A Charnois, Thierry
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zaratiana-etal-2022-gnner
%X 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.
%R 10.18653/v1/2022.acl-srw.9
%U https://aclanthology.org/2022.acl-srw.9/
%U https://doi.org/10.18653/v1/2022.acl-srw.9
%P 97-103

Markdown (Informal)

[GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks](https://aclanthology.org/2022.acl-srw.9/) (Zaratiana et al., ACL 2022)

• GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks (Zaratiana et al., ACL 2022)

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.