SQGE:支持查询原型指导和增强为几次射击的关系三重提取
Chen Gao1, Xuan Zhang2, Zhi Jin3
1School of Information Science and Engineering, Yunnan University, Yunnan 650091, China.
概括
本研究引入了一种新的几次射击关系三重提取 (FS-RTE) 方法,以解决数据稀缺问题. 拟议的SQGE方法有效地减少了类内和类间的差距,提高了提取精度.
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:
- 使用原型网络的短拍关系三重提取 (FS-RTE) 方法由于数据有限而面临挑战.
- 支持集中的数据稀缺导致类内和类间的差距,阻碍了准确的特征表示和分类.
研究的目的:
- 提出一种新的FS-RTE方法,支持查询原型指导和增强 (SQGE),以克服数据稀缺的局限性.
- 通过改善支持和查询集之间的特征匹配来减少类内差距.
- 通过增强实体表示和利用对比学习来缓解类间的差距.
主要方法:
- 一个支持查询原型指南模块融合了支持和查询原型,以更好地捕捉基本特征.
- 实体级特征增强细化了同一类内的目标实体的表示.
- 相反的学习构建了积极和消极的实例,以加强 intra-class 代表性和区分类别.
主要成果:
- 在FS-RTE中,SQGE方法在减少类内和类间差距方面表现出显著的有效性.
- 三个数据集的实验结果验证了拟议方法的卓越性能.
- 该方法在低数据场景中成功提高了关系三重提取的准确性.
结论:
- 拟议的SQGE方法为少数拍摄的关系三重提取提供了一个强大的解决方案.
- SQGE有效地解决了数据稀缺所带来的挑战,从而提高了模型性能.
- 该方法为低资源信息提取领域做出了有价值的贡献.
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