山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (2): 70-76.doi: 10.6040/j.issn.1672-3961.0.2022.086
宋佳芮1,2,陈艳平1,2*,王凯1,2,黄瑞章1,2,秦永彬1,2
SONG Jiarui1,2, CHEN Yanping1,2*, WANG Kai1,2, HUANG Ruizhang1,2, QIN Yongbin1,2
摘要： 针对现有命名实体识别方法存在的语义信息获取不全面问题,提出基于Affix-Attention的命名实体识别语义补充方法。将句子和句子中每个单词对应的词缀输入到编码层,使用Bi-LSTM提取上下文特征。在编码层设计特征融合模块、建模文本特征与词缀特征的对应关系,使用Affix-Attention同时关注文本信息和词缀信息进行语义补充。解码层使用CRF层得到目标序列。在生物医学领域的JNLPBA-2004和BC2GM基准数据集上的试验结果综合评价指标F1达到81.73%、84.73%;在公共数据集CONLL-2003中试验结果综合评价指标F1达到91.35%。试验结果表明,本研究方法能够有效获取词的内部语义特征,融合文本信息和词缀信息,达到语义补充的效果,提升命名实体识别的性能。
中图分类号:
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