Entity Recognition for Interpretation of Bone-sign Integrated with Multiple Features
This study constructs a named entity recognition(NER)model suitable for the bone-sign interpretations of Han Chang'an City to solve the problem of the inability to classify some bone-sign interpretations due to the lack of key content.The original text of the bone-sign interpretations of Han Chang'an City is used as the dataset,and the begin,inside,outside,end(BIOE)annotation method is utilized to annotate the bone-sign interpretation entities.A multi-feature fusion network(MFFN)model is proposed,which not only considers the structural features of individual characters but also integrates the structural features of character-word combinations to enhance the model's comprehension of the bone-sign interpretations.The experimental results demonstrate that the MFFN model can better identify the named entities of the bone-sign interpretations of Han Chang'an City and classify the bone-sign interpretations,outperforming existing NER models.This model provides historians and researchers with richer and more precise data support.
bone-signentity recognitionBIOE annotation methodmultiple features fusionclassification of interpretation
万方数据
维普
