脚本语言
计算机科学
甲骨文公司
任务(项目管理)
人工智能
深度学习
工作量
自然语言处理
程序设计语言
管理
经济
操作系统
作者
Yixin Xu,Yuan Feng,Jiahao Liu,Shengyu Song,Zhibin Xu,Lan Zhang
标识
DOI:10.1007/978-3-031-40286-9_9
摘要
Oracle Bone Characters (OBC) are the oldest developed pictographs in China, having been created over 3000 years ago. As a result of their antiquity and the scarcity of relevant historical sources, identifying and interpreting OBC has been an ongoing challenge for scholars of oracle bone characters. Large Seal Script evolved from OBC, and retains many of its features, making the linking of these two scripts an urgent task. The traditional method of textual study and interpretation can be time-consuming, requires a high degree of professionalism, and demands significant material and human resources. To address these issues, we propose the Conf-UNet model, a deep learning approach that incorporates a U-net combined with a multi-head self-attention mechanism. This model was used to link Large Seal Script with unidentified oracle bone characters to reduce the workload and provide linguists with a reliable method to speculate the sealed characters that correlate with unidentified OBC. The proposed model also enables linguists to use deep learning to research the evolution of pictographs. Experiments on the HWOBC-A dataset demonstrate that our model outperforms other models on this task of identifying oracle bone characters.
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