有效的句子级关系提取模型使用以实体为中心的依赖树
1Department of Artificial Intelligence, Konkuk University, Seoul, Republic of South Korea.
PeerJ. Computer science
|September 24, 2024
概括
本研究引入了一个以实体为中心的依赖树,用于关系提取,改进语法和词汇信息捕获. 这种新型模型可以高效地实现最先进的结果,减少对资源的需求.
科学领域:
- 自然语言处理自然语言处理.
- 提取信息 提取信息
- 计算语言学 计算语言学
背景情况:
- 传统的基于依赖关系的关系提取方法面临着硬修剪 (丢失信息) 和软修剪 (削弱语法) 的局限性.
- 向基于语言模型 (LM) 的方法的转变增加了资源消耗和计算成本,但仅带来了边际收益.
- 现有的方法很难有效地平衡语法信息和词汇细节.
研究的目的:
- 提出一种有效的关系提取模型,克服现有方法的局限性.
- 为了同时增强语法和词汇信息的捕获.
- 减少对大型语言模型和附加数据的依赖.
主要方法:
- 开发了一个以实体为中心的依赖树,用实体作为根节点重建依赖树.
- 提出了一个新的模型,将以实体为中心的依赖树与语言模型集成在一起.
- 在标准数据集上评估模型:TACRED,Re-TACRED和SemEval 2010第8任务.
主要成果:
- 获得了最先进的F1分数:在TACRED上达到74.9%,在Re-TACRED上达到91.2%,在SemEval 2010任务8上达到90.5%.
- 证明有效地捕获语法信息,而不会失去词汇细节.
- 展示了有效的关系提取,而不需要更大的模型或额外的数据.
结论:
- 以实体为中心的依赖树方法为关系提取提供了一个强大的解决方案.
- 拟议的模型为当前基于LM的方法提供了一个计算效率高和高性能替代方案.
- 这种方法通过优化语法和词汇特征的使用来推进关系提取.
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