融合框架:条件意识的一级嵌套事件提取模型
Sen Niu1, Xiaohong Han1, Liu Cao2
1Department of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, China.
Journal of biomedical informatics
|December 26, 2025
概括
我们开发了一种条件意识单阶段模型 (CA-NEE) 用于生物医学事件提取. 该模型有效地识别了复杂的重叠和嵌套事件,改善了触发器和参数的分类.
科学领域:
- 生物医学自然语言处理
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 从文本中提取生物医学事件对于理解生物过程至关重要.
- 现有的模型难以处理复杂的事件结构,如重叠和嵌套事件.
- 准确识别事件触发器,论点和角色是具有挑战性的.
研究的目的:
- 引入CA-NEE,一种用于生物医学事件提取的新型单阶段模型.
- 解决处理重叠和嵌套生物医学事件的局限性.
- 在复杂的场景中提高触发器和参数分类的准确性.
主要方法:
- 开发了一种条件意识单阶段模型 (CA-NEE).
- 集成了一个事件类型意识的调节机制与代币对关系建模.
- 采用条件层规范化 (CLN) 进行动态代币表示适应.
- 使用并行词对得分器来同时进行跨度和角色预测.
主要成果:
- 在触发分类 (TC) 和参数分类 (AC) 中,CA-NEE 显示出一致的性能增长.
- 在复杂的重叠和嵌套事件结构中观察到显著的改善.
- 对GENIA11和GENIA13数据集的评估证实了该模型对基线的有效性.
结论:
- CA-NEE为生物医学事件提取提供了有效和高效的解决方案.
- 该模型的架构成功地处理了复杂的事件结构.
- 这种方法推动了自动化生物医学信息提取领域的发展.
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