语义增强图形神经网络用于古代中国书籍中命名实体识别
Yongrui Xu1, Caixia Mao2, Zhiyong Wang3
1School of Computer Science and Technology, Hubei University of Science and Technology, Xianning, 437100, China.
Scientific reports
|July 30, 2024
概括
这项研究通过使用图形神经网络整合外部知识来增强古代中国命名实体识别 (NER). 这种新的方法提高了模型性能,克服了历史文本中数据稀缺性的挑战.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 数字人文学科 数字人文学科
背景情况:
- 命名实体识别 (NER) 对于从古代中国文本中提取知识至关重要.
- 挑战包括语言特征 (单个字符,短句) 和有限的培训数据.
- 现有的深度学习模型在古代中国字符的语义表示方面扎.
研究的目的:
- 通过结合外部知识,对古代中国书籍的NER进行语义增强.
- 解决当前模型在处理数据稀缺性和语言复杂性方面的局限性.
- 提高从中国历史文献中提取知识的准确性和效率.
主要方法:
- 提出了一个基于图形神经网络 (GNN) 的新型模型.
- 整合了两种形式的外部知识:词典级和章节级信息.
- 利用图表注意力机制 (GAT) 将外部知识纳入模型的输入上下文.
主要成果:
- 在C_CLUE数据集上,与基线BAC-CRF模型相比,实现了3.82%的改善.
- 超过了几种最先进的字典增强模型.
- 证明了古代中国NER的有效语义增强.
结论:
- 拟议的GNN模型有效地整合了外部知识,以改进古代中国的NER.
- 该方法成功地减轻了数据稀缺性和语言特征带来的挑战.
- 这项工作推进了从历史文本中提取知识的领域.
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