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        <title>Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications</title>
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        <namePart type="given">Jatin</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
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        <name type="personal">
            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
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    <abstract>In this work, we address the NER problem by splitting it into two logical sub-tasks: (1) Span Detection which simply extracts entity mention spans irrespective of entity type; (2) Span Classification which classifies the spans into their entity types. Further, we formulate both sub-tasks as question-answering (QA) problems and produce two leaner models which can be optimized separately for each sub-task. Experiments with four cross-domain datasets demonstrate that this two-step approach is both effective and time efficient. Our system, SplitNER outperforms baselines on OntoNotes5.0, WNUT17 and a cybersecurity dataset and gives on-par performance on BioNLP13CG. In all cases, it achieves a significant reduction in training time compared to its QA baseline counterpart. The effectiveness of our system stems from fine-tuning the BERT model twice, separately for span detection and classification. The source code can be found at https://github.com/c3sr/split-ner.</abstract>
    <identifier type="citekey">arora-park-2023-split</identifier>
    <identifier type="doi">10.18653/v1/2023.acl-short.36</identifier>
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        <url>https://aclanthology.org/2023.acl-short.36</url>
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        <date>2023-07</date>
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            <start>416</start>
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%0 Conference Proceedings
%T Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications
%A Arora, Jatin
%A Park, Youngja
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F arora-park-2023-split
%X In this work, we address the NER problem by splitting it into two logical sub-tasks: (1) Span Detection which simply extracts entity mention spans irrespective of entity type; (2) Span Classification which classifies the spans into their entity types. Further, we formulate both sub-tasks as question-answering (QA) problems and produce two leaner models which can be optimized separately for each sub-task. Experiments with four cross-domain datasets demonstrate that this two-step approach is both effective and time efficient. Our system, SplitNER outperforms baselines on OntoNotes5.0, WNUT17 and a cybersecurity dataset and gives on-par performance on BioNLP13CG. In all cases, it achieves a significant reduction in training time compared to its QA baseline counterpart. The effectiveness of our system stems from fine-tuning the BERT model twice, separately for span detection and classification. The source code can be found at https://github.com/c3sr/split-ner.
%R 10.18653/v1/2023.acl-short.36
%U https://aclanthology.org/2023.acl-short.36
%U https://doi.org/10.18653/v1/2023.acl-short.36
%P 416-426

Markdown (Informal)

[Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications](https://aclanthology.org/2023.acl-short.36) (Arora & Park, ACL 2023)

• Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications (Arora & Park, ACL 2023)

ACL

• Jatin Arora and Youngja Park. 2023. Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 416–426, Toronto, Canada. Association for Computational Linguistics.