计算机科学
知识图
构造(python库)
图形
自然语言处理
管道(软件)
情报检索
人工智能
领域(数学)
可视化
命名实体识别
理论计算机科学
任务(项目管理)
程序设计语言
经济
管理
纯数学
数学
作者
Qingyan Guo,Yang Sun,Guanzhong Liu,Zijun Wang,Zijing Ji,Yuxin Shen,Xin Wang
标识
DOI:10.1007/978-3-030-87571-8_28
摘要
Knowledge graph construction (KGC) aims to organize knowledge into a semantic network which can reveal relations between entities. Its basis is named entity recognition (NER) and relation extraction (RE) tasks. In recent years, KGC methods for Chinese have made great progress. However, most existing methods concentrate on modern Chinese and ignore the classical Chinese due to its complexity, making research in this field relatively lacking. In this paper, we construct a high-quality classical Chinese labeled dataset for NER and RE tasks. More specifically, we conduct a series of experiments to select an optimal NER model to strengthen the whole pipeline model for NER and RE tasks, augmenting our dataset iteratively and automatically. Additionally, we propose an improved RE model to better combine semantic entity information extracted by the NER model. Moreover, we construct a knowledge graph (KG) based on Chinese historical literature and design a visualization system with intuitive display and query functions.
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