@inproceedings{cairang-etal-2024-rong,
title = "融合多元特征表示的藏文命名实体识别方法赵小兵{\ensuremath{*}}2(Research on {T}ibetan Named Entity Recognition Using Multi-Feature Fusion Representation)",
author = "Cairang, Ejian and
Maoke, Zhou and
Bo, Chen and
Xiaobing, Zhao",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.26/",
pages = "340--351",
language = "zho",
abstract = "{\textquotedblleft}本文针对基于音节嵌入方式的藏文命名实体识别(TNER)中词汇信息和音节部件信息忽略的问题,提出了基于交叉Transformer架构的MECT-TL模型,融合了藏文音节信息、词汇信息和音节部件信息的多元数据特征。MECT-TL通过平面网络结构将藏文音节与词汇信息结合,并整合音节部件信息,有效提升了藏文实体识别的准确性。实验结果显示,相较于主流的TNER基准模型BiLSTM-CRF,本文模型在F1值上提高了5.14个百分点,与基于Transformer架构的TENER模型相比提高了4.18个百分点。这表明,融合藏文词汇和音节部件信息的方法可以显著提高TNER任务的性能。{\textquotedblright}"
}
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<abstract>“本文针对基于音节嵌入方式的藏文命名实体识别(TNER)中词汇信息和音节部件信息忽略的问题,提出了基于交叉Transformer架构的MECT-TL模型,融合了藏文音节信息、词汇信息和音节部件信息的多元数据特征。MECT-TL通过平面网络结构将藏文音节与词汇信息结合,并整合音节部件信息,有效提升了藏文实体识别的准确性。实验结果显示,相较于主流的TNER基准模型BiLSTM-CRF,本文模型在F1值上提高了5.14个百分点,与基于Transformer架构的TENER模型相比提高了4.18个百分点。这表明,融合藏文词汇和音节部件信息的方法可以显著提高TNER任务的性能。”</abstract>
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%0 Conference Proceedings
%T 融合多元特征表示的藏文命名实体识别方法赵小兵\ensuremath*2(Research on Tibetan Named Entity Recognition Using Multi-Feature Fusion Representation)
%A Cairang, Ejian
%A Maoke, Zhou
%A Bo, Chen
%A Xiaobing, Zhao
%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 zho
%F cairang-etal-2024-rong
%X “本文针对基于音节嵌入方式的藏文命名实体识别(TNER)中词汇信息和音节部件信息忽略的问题,提出了基于交叉Transformer架构的MECT-TL模型,融合了藏文音节信息、词汇信息和音节部件信息的多元数据特征。MECT-TL通过平面网络结构将藏文音节与词汇信息结合,并整合音节部件信息,有效提升了藏文实体识别的准确性。实验结果显示,相较于主流的TNER基准模型BiLSTM-CRF,本文模型在F1值上提高了5.14个百分点,与基于Transformer架构的TENER模型相比提高了4.18个百分点。这表明,融合藏文词汇和音节部件信息的方法可以显著提高TNER任务的性能。”
%U https://aclanthology.org/2024.ccl-1.26/
%P 340-351
Markdown (Informal)
[融合多元特征表示的藏文命名实体识别方法赵小兵∗2(Research on Tibetan Named Entity Recognition Using Multi-Feature Fusion Representation)](https://aclanthology.org/2024.ccl-1.26/) (Cairang et al., CCL 2024)
ACL