文件级关系提取与实体类型约束
Ridong Han1, Tao Peng2, Beibei Zhu3
1School of Computer Science and Technology/School of Artificial Intelligence, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China; College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
本研究引入了一种新的方法,通过使用实体类型约束来学习关系相关性来改进文档级关系提取. 这种方法有效地解决了长尾问题和关系提取任务中的多标签分类等挑战.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 文档级别的关系提取面临着长尾和多标签问题的挑战.
- 使用变压器或文档图形的现有方法很难有效地解决这些问题.
研究的目的:
- 提出一种使用实体类型约束来学习关系相关性的新方法,以改善文档级关系提取.
- 解决当前处理长尾和多标签关系提取场景的方法的局限性.
主要方法:
- 从全球和地方的角度来看,学习关系的相关性.
- 构建实体类型和关系之间的类型受约束图,以实现全球相关性.
- 提出一种类型受约束的损失,以提高本地匹配关系的分类概率.
主要成果:
- 拟议的模型显著优于DocRED和DWIE数据集的现有基线.
- 在处理长尾和多标签关系提取设置方面证明有效.
- 实体类型约束成功指导学习关系相关性.
结论:
- 通过实体类型约束来学习关系相关性是文档级关系提取的一个有希望的方向.
- 拟议的方法为应对长尾和多标签挑战提供了强大的解决方案.
- 这项工作推进了从文件中提取信息的最新技术.
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