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    <titleInfo>
        <title>A Multi-Task Biomedical Named Entity Recognition Method Based on Data Augmentation</title>
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    <name type="personal">
        <namePart type="given">Zhao</namePart>
        <namePart type="family">Hui</namePart>
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        <namePart type="given">Zhao</namePart>
        <namePart type="family">Di</namePart>
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    <name type="personal">
        <namePart type="given">Meng</namePart>
        <namePart type="family">Jiana</namePart>
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    <name type="personal">
        <namePart type="given">Liu</namePart>
        <namePart type="family">Shuang</namePart>
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    <name type="personal">
        <namePart type="given">Lin</namePart>
        <namePart type="family">Hongfei</namePart>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
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            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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        <name type="personal">
            <namePart type="given">Jiye</namePart>
            <namePart type="family">Liang</namePart>
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        <name type="personal">
            <namePart type="given">Xianpei</namePart>
            <namePart type="family">Han</namePart>
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        <name type="personal">
            <namePart type="given">Zhiyuan</namePart>
            <namePart type="family">Liu</namePart>
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        <name type="personal">
            <namePart type="given">Yulan</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>“The rapid development of artificial intelligence has led to an explosion of literature in the biomed-ical field, and Biomedical Named Entity Recognition (BioNER) can quickly and accurately iden-tify key information from unstructured text. This task has become an important topic to promotethe rapid development of intelligence in the biomedical field. However, in the Named EntityRecognition (NER) of the biomedical field, there are always some problems of unclear boundaryrecognition, the underutilization of hierarchical information in sentences and the scarcity of train-ing data resources. Based on this, this paper proposes a multi-task BioNER model based on dataaugmentation, using four data augmentation methods: Mention Replacement (MR), Label-wisetoken Replacement (LwTR), Shuffle Within Segments (SiS) and Synonym Replacement (SR)to increase the training data. The syntactic information is extracted by incorporating the inputsentence into the Graph Convolutional Network (GCN), and then the tag information encodedby BERT is interacted through a co-attention mechanism to obtain an interaction matrix. Subse-quently, NER is performed through boundary detection tasks and span classification tasks. Com-parative experiments with other methods are conducted on the BC5CDR and JNLPBA datasets,as well as the CCKS2017 dataset. The experimental results demonstrate the effectiveness of themodel proposed in this paper.”</abstract>
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        <url>https://aclanthology.org/2024.ccl-1.83/</url>
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        <date>2024-07</date>
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            <start>1075</start>
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%0 Conference Proceedings
%T A Multi-Task Biomedical Named Entity Recognition Method Based on Data Augmentation
%A Hui, Zhao
%A Di, Zhao
%A Jiana, Meng
%A Shuang, Liu
%A Hongfei, Lin
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F hui-etal-2024-multi
%X “The rapid development of artificial intelligence has led to an explosion of literature in the biomed-ical field, and Biomedical Named Entity Recognition (BioNER) can quickly and accurately iden-tify key information from unstructured text. This task has become an important topic to promotethe rapid development of intelligence in the biomedical field. However, in the Named EntityRecognition (NER) of the biomedical field, there are always some problems of unclear boundaryrecognition, the underutilization of hierarchical information in sentences and the scarcity of train-ing data resources. Based on this, this paper proposes a multi-task BioNER model based on dataaugmentation, using four data augmentation methods: Mention Replacement (MR), Label-wisetoken Replacement (LwTR), Shuffle Within Segments (SiS) and Synonym Replacement (SR)to increase the training data. The syntactic information is extracted by incorporating the inputsentence into the Graph Convolutional Network (GCN), and then the tag information encodedby BERT is interacted through a co-attention mechanism to obtain an interaction matrix. Subse-quently, NER is performed through boundary detection tasks and span classification tasks. Com-parative experiments with other methods are conducted on the BC5CDR and JNLPBA datasets,as well as the CCKS2017 dataset. The experimental results demonstrate the effectiveness of themodel proposed in this paper.”
%U https://aclanthology.org/2024.ccl-1.83/
%P 1075-1086

Markdown (Informal)

[A Multi-Task Biomedical Named Entity Recognition Method Based on Data Augmentation](https://aclanthology.org/2024.ccl-1.83/) (Hui et al., CCL 2024)

• A Multi-Task Biomedical Named Entity Recognition Method Based on Data Augmentation (Hui et al., CCL 2024)

ACL

• Zhao Hui, Zhao Di, Meng Jiana, Liu Shuang, and Lin Hongfei. 2024. A Multi-Task Biomedical Named Entity Recognition Method Based on Data Augmentation. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1075–1086, Taiyuan, China. Chinese Information Processing Society of China.