关系提取:通过深度学习和实体相关特征的进步
Youwen Zhao1, Xiangbo Yuan1, Ye Yuan1
1Digital and information management center, Kweichow Moutai Co., Ltd, Zunyi, 564501 Guizhou China.
Social network analysis and mining
|June 16, 2023
概括
这项研究引入了一种新的关系提取方法,将卷积神经网络和图形卷积神经网络融合起来,以捕捉实体语义和结构. 该方法显著提高了在多个数据集上的关系提取性能.
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:
- 关系提取对于理解文本至关重要,但由于对实体对的语义和结构信息有限,它面临着挑战.
- 现有的方法很难有效地利用语义和结构特征来准确识别关系.
研究的目的:
- 通过有效地融合与实体相关的语义和结构特征,开发一种强大的关系提取方法.
- 增强提取高阶抽象特征,以提高关系识别准确度.
主要方法:
- 提出了一种结合卷积神经网络 (CNNs) 和图形卷积神经网络 (GCNs) 的新方法.
- 与实体相关的特征被合并以产生全面的"合并特征".
- 应用了深度学习框架来提取关系提取的高阶抽象特征.
主要成果:
- 提出的方法在三个公共数据集上获得了高F1分数:ACE05英语分数为77.70%,ACE05中文分数为90.12%,SanWen分数为68.84%.
- 实验结果证明了基于融合的深度学习方法的有效性和稳定性.
- 该方法成功地捕获了关键的语义和结构,以改善关系提取.
结论:
- 提出的方法有效地解决了通过融合语义和结构信息来提取关系的局限性.
- 在关系提取任务中,CNN和GCN的融合为深度特征提取提供了一个强大的框架.
- 这种方法显示出在各种自然语言处理应用中推进关系提取领域的重大前景.
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