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    <titleInfo>
        <title>MRC-based Medical NER with Multi-task Learning and Multi-strategies</title>
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    <name type="personal">
        <namePart type="given">Xiaojing</namePart>
        <namePart type="family">Du</namePart>
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        <namePart type="given">Jia</namePart>
        <namePart type="family">Yuxiang</namePart>
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    <name type="personal">
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        <namePart type="family">Hongying</namePart>
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        <dateIssued>2022-10</dateIssued>
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            <title>Proceedings of the 21st Chinese National Conference on Computational Linguistics</title>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>“Medical named entity recognition (NER), a fundamental task of medical information extraction, is crucial for medical knowledge graph construction, medical question answering, and automatic medical record analysis, etc. Compared with named entities (NEs) in general domain, medical named entities are usually more complex and prone to be nested. To cope with both flat NEs and nested NEs, we propose a MRC-based approach with multi-task learning and multi-strategies. NER can be treated as a sequence labeling (SL) task or a span boundary detection (SBD) task. We integrate MRC-CRF model for SL and MRC-Biaffine model for SBD into the multi-task learning architecture, and select the more efficient MRC-CRF as the final decoder. To further improve the model, we employ multi-strategies, including adaptive pre-training, adversarial training, and model stacking with cross validation. Experiments on both nested NER corpus CMeEE and flat NER corpus CCKS2019 show the effectiveness of the MRC-based model with multi-task learning and multi-strategies.”</abstract>
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        <url>https://aclanthology.org/2022.ccl-1.74</url>
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        <date>2022-10</date>
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            <start>836</start>
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%0 Conference Proceedings
%T MRC-based Medical NER with Multi-task Learning and Multi-strategies
%A Du, Xiaojing
%A Yuxiang, Jia
%A Hongying, Zan
%S Proceedings of the 21st Chinese National Conference on Computational Linguistics
%D 2022
%8 October
%I Chinese Information Processing Society of China
%C Nanchang, China
%G English
%F du-etal-2022-mrc
%X “Medical named entity recognition (NER), a fundamental task of medical information extraction, is crucial for medical knowledge graph construction, medical question answering, and automatic medical record analysis, etc. Compared with named entities (NEs) in general domain, medical named entities are usually more complex and prone to be nested. To cope with both flat NEs and nested NEs, we propose a MRC-based approach with multi-task learning and multi-strategies. NER can be treated as a sequence labeling (SL) task or a span boundary detection (SBD) task. We integrate MRC-CRF model for SL and MRC-Biaffine model for SBD into the multi-task learning architecture, and select the more efficient MRC-CRF as the final decoder. To further improve the model, we employ multi-strategies, including adaptive pre-training, adversarial training, and model stacking with cross validation. Experiments on both nested NER corpus CMeEE and flat NER corpus CCKS2019 show the effectiveness of the MRC-based model with multi-task learning and multi-strategies.”
%U https://aclanthology.org/2022.ccl-1.74
%P 836-847

Markdown (Informal)

[MRC-based Medical NER with Multi-task Learning and Multi-strategies](https://aclanthology.org/2022.ccl-1.74) (Du et al., CCL 2022)

• MRC-based Medical NER with Multi-task Learning and Multi-strategies (Du et al., CCL 2022)

ACL

• Xiaojing Du, Jia Yuxiang, and Zan Hongying. 2022. MRC-based Medical NER with Multi-task Learning and Multi-strategies. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 836–847, Nanchang, China. Chinese Information Processing Society of China.