基于LEBERT的时间信息增强中文命名实体识别
Enhanced Chinese named entity recognition based on LEBERT time information
张光明 1肖然 2张弛 1高谦 1谈栋华 1彭菊红3
作者信息
- 1. 湖北大学人工智能学院,湖北 武汉 430062
- 2. 湖北大学计算机与信息工程学院,湖北 武汉 430062
- 3. 湖北大学人工智能学院,湖北 武汉 430062;智能感知系统与安全教育部重点实验室(湖北大学),湖北 武汉 430062
- 折叠
摘要
为解决现有预训练模型没有充分考虑汉语词汇的分割特征的问题,提出一种基于 LEBERT-ILRN-RA-CRF 的中文命名实体识别模型.该模型首先利用 LEBERT对输入文本进行词汇表增强和嵌入处理,然后利用 ILRN模块提取时间信息,并将字符-词融合与字向量采集相结合,最后使用条件随机场(CRF)进行结果校正.此外,该模型还引入了词汇量增强和残差门控注意网络来加强时间特征的提取和局部特征的表达,进一步提升了模型的识别效果.在 Weibo 和Resume两个数据集上分别取得了 71.73%和 96.51%的宏 F1 值.实验表明该模型可以考虑到汉语词汇的分割问题,改善中文命名实体识别任务的识别效果.
Abstract
In order to solve the problem that the existing pre-trained models do not fully consider the segmentation features of Chinese words,a Chinese named entity recognition model based on LEBERT-ILRN-RA-CRF was proposed.The model firstly used LEBERT to enhance and embed the input text,then used ILRN module to extract time information,combined character-word fusion with word vector acquisition,and finally used conditional random field(CRF)to correct the result.In addition,the model also introduced vocabulary enhancement and residual gated attention network to strengthen the extraction of temporal features and the expression of local features,which further improved the recognition effect of the model.The macro F1 values of 71.73%and 96.51%were obtained on the Weibo and Resume datasets,respectively.Experiments show that the model can take into account the segmentation problem of Chinese vocabulary and improve the recognition effect of Chinese named entity recognition task.
关键词
中文命名实体识别/时间信息增强/LEBERT/CRF/残差门控注意力机制Key words
Chinese named entity recognition/temporal information enhancement/LEBERT/CRF/residual-gated attention mechanism引用本文复制引用
出版年
2025
NETL
NSTL
万方数据