文档级别的关系提取与关系对应关系
Ridong Han1, Tao Peng1, Benyou Wang2
1College of Computer Science and Technology, Jilin University, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China.
概括
本研究引入了关系同时发生的相关性,以改善文档层面的关系提取,有效地解决长尾和多标签的挑战,以便更好地转移知识和识别关系.
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
- 机器学习 机器学习
背景情况:
- 文档级关系提取在长尾和多标签数据方面面临挑战.
- 现有的方法主要集中在实体对表示上,忽视了这些具体问题.
研究的目的:
- 在文档级别的关系提取中引入关系并发相关性.
- 为了利用这些相关性进行知识转移和改进多标签分类.
主要方法:
- 分析和结合关系的同时发生的相关性.
- 使用关系嵌入,并提出两个共同发生预测子任务 (粗粒和细粒).
- 使用学习的相关性意识嵌入来指导关系事实提取.
主要成果:
- 与基线方法相比,在DocRED和DWIE数据集上取得了优异的性能.
- 证明了关系相关性在解决长尾和多标签问题的有效性.
- 通过实质性实验和深入分析来验证.
结论:
- 关系同时发生的相关性提供了一个有前途的方法来增强文档级别的关系提取.
- 提出的方法有效地解决了数据稀缺问题,并改善了语义相关关系的识别.
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