开放世界的半监督关系提取
Diange Zhou1, Yilin Duan1, Shengwen Li2
1School of Computer Science, China University of Geosciences, Wuhan, 430078, China.
概括
本研究引入了一种新的开放世界的半监督关系提取方法 (SIND),可以有效地从文本中识别已知和新鲜的关系,提高现实世界的应用的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 半监督关系提取方法通过使用标记和未标记的数据来提高准确性.
- 现有的方法在现实世界场景中失败,原因是封闭世界的假设,排除了新的关系.
研究的目的:
- 提出一个开放世界的半监督关系提取任务来处理新鲜的关系.
- 引入一种新的方法,SIND,用于同时提取已见和新的关系.
主要方法:
- SIND使用对比式学习来增强语义关系表示.
- 一种集群意识方法使用特征空间相似性发现了新的关系.
- 最大的理论解决了从未标记的新课程的学习不平衡.
主要成果:
- 在三个基准数据集上,SIND的表现明显优于基线模型.
- 在提取现有和以前未见的关系方面表现出有效性.
结论:
- 在开放世界的环境中,SIND提供了一个强大的解决方案,用于在未注释的数据中发现关系.
- 为NLP任务提供了有价值的参考,例如文本分类和命名实体识别.
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