Cairang Ejian

Also published as: 才让 俄见

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2024

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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)
Cairang Ejian (俄见才让) | Maoke Zhou (周毛克) | Bo Chen (陈波) | Xiaobing Zhao (赵小兵)
Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)

“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。”
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