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Received:March 05, 2024 Revised:April 03, 2024
Received:March 05, 2024 Revised:April 03, 2024
中文摘要: 构建适用于汉长安城骨签释文的命名实体识别模型, 用来解决由于汉长安城骨签释文关键内容缺失, 而导致无法对部分骨签释文进行分类的问题. 本文将汉长安城骨签释文原始文本作为数据集, 采用BIOE (begin, inside, outside, end)标注方法对释文实体进行数据标注, 并提出融合字结构特征、字词结构特征的多特征融合网络模型(multi-feature fusion network, MFFN). 该模型不仅考虑了单个字符的结构特征, 还融合了字与词的结构特征, 以增强模型对骨签释文的理解能力. 实验结果表明, MFFN模型能够更好地识别汉长安城骨签释文的命名实体, 实现骨签释文分类, 优于现有NER模型, 为历史学家和研究人员提供更加丰富和准确的数据支持.
Abstract: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.
keywords: bone-sign entity recognition BIOE annotation method multiple features fusion classification of interpretation
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基金项目:国家社科基金冷门绝学研究专项(20VJXT001)
引用文本:
石雨梦,王慧琴,王展,刘瑞,王可.融合多特征的骨签释文实体识别.计算机系统应用,2024,33(9):38-47
SHI Yu-Meng,WANG Hui-Qin,WANG Zhan,LIU Rui,WANG Ke.Entity Recognition for Interpretation of Bone-sign Integrated with Multiple Features.COMPUTER SYSTEMS APPLICATIONS,2024,33(9):38-47
石雨梦,王慧琴,王展,刘瑞,王可.融合多特征的骨签释文实体识别.计算机系统应用,2024,33(9):38-47
SHI Yu-Meng,WANG Hui-Qin,WANG Zhan,LIU Rui,WANG Ke.Entity Recognition for Interpretation of Bone-sign Integrated with Multiple Features.COMPUTER SYSTEMS APPLICATIONS,2024,33(9):38-47