山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (1): 91-99.doi: 10.6040/j.issn.1672-3961.0.2022.350
李明键1,李卫军1,2*,王海荣1,2
LI Mingjian1, LI Weijun1,2*, WANG Hairong1,2
摘要： 为了提升GlobaiPointer方法的实体边界区分性能,提出一种融合词汇信息与GlobalPointer的实体识别方法。对SoftLexicon提取的词汇特征与字符相结合,采用BiLSTM网络与RoPE编码捕捉时序与相对位置信息构建全面特征,通过实体矩阵实现实体识别。对多个数据集进行试验,本研究提出的模型相较于其他基线模型,精确率、召回率、F1均有一定的提升,Weibo数据集中F1达到71.33%、CMeEE数据集中F1达到63.45%,表明本研究提出的模型架构能够进一步扩充语义表征,增强识别性能。
中图分类号:
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