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        <title>多机制整合的中文医疗命名实体识别(Infusing multi-schemes for Chinese Medical Named Entity Recognition)</title>
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    <abstract>“在互联网在线医疗领域,由于大多数患者缺乏医学培训,以及不同学科病理特征的复杂性,医患对话文本中的医学命名实体呈现出长且多词的句法特点,给命名实体识别算法提出了新的挑战。 为解决这一问题,本研究融合多个不同粒度的扩张卷积机制,构建了Flat-Lattice-CNN模型。 该模型不仅考虑字符和词语的语义信息以及它们的绝对和相对位置信息,还提取跨越不同距离的多个字符/词语的共现依存关系特征,以此提高医学长命名实体的识别精度。 实验结果表明,本文提出的模型在所评估数据集的命名实体识别任务上有普遍性的性能提升,尤其是在以长实体为主的中文医疗数据集CTDD上,该模型的F 1值提升了约2%,具有更优的表现。”</abstract>
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%0 Conference Proceedings
%T 多机制整合的中文医疗命名实体识别(Infusing multi-schemes for Chinese Medical Named Entity Recognition)
%A Wang, Shanshan
%A Zhang, Kunyuan
%A Yan, Rong
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%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 zho
%F shanshan-etal-2024-duo
%X “在互联网在线医疗领域,由于大多数患者缺乏医学培训,以及不同学科病理特征的复杂性,医患对话文本中的医学命名实体呈现出长且多词的句法特点,给命名实体识别算法提出了新的挑战。 为解决这一问题,本研究融合多个不同粒度的扩张卷积机制,构建了Flat-Lattice-CNN模型。 该模型不仅考虑字符和词语的语义信息以及它们的绝对和相对位置信息,还提取跨越不同距离的多个字符/词语的共现依存关系特征,以此提高医学长命名实体的识别精度。 实验结果表明,本文提出的模型在所评估数据集的命名实体识别任务上有普遍性的性能提升,尤其是在以长实体为主的中文医疗数据集CTDD上,该模型的F 1值提升了约2%,具有更优的表现。”
%U https://aclanthology.org/2024.ccl-1.29/
%P 382-393

Markdown (Informal)

[多机制整合的中文医疗命名实体识别(Infusing multi-schemes for Chinese Medical Named Entity Recognition)](https://aclanthology.org/2024.ccl-1.29/) (Wang et al., CCL 2024)

• 多机制整合的中文医疗命名实体识别(Infusing multi-schemes for Chinese Medical Named Entity Recognition) (Wang et al., CCL 2024)

ACL

• Shanshan Wang, Kunyuan Zhang, and Rong Yan. 2024. 多机制整合的中文医疗命名实体识别(Infusing multi-schemes for Chinese Medical Named Entity Recognition). In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 382–393, Taiyuan, China. Chinese Information Processing Society of China.