Objective:

Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction.

Material and Methods:

We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events.

Results:

Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13).

Conclusions:

The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.

中文翻译：

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CMBEE：基于约束的生物医学事件提取多任务学习框架

客观的：

事件提取在自然语言处理中起着至关重要的作用。然而，在生物医学领域，与单个事件相比，嵌套事件的存在增加了事件提取的复杂性，并且这些事件通常具有很强的语义关系和约束。以前的方法忽略了这些复杂的嵌套事件之间的绑定连接。本研究旨在开发一个基于事件约束信息的统一框架，联合提取生物医学事件触发器和论点，并提高嵌套生物医学事件提取的性能。

材料与方法：

我们提出了一种基于约束信息的多任务学习框架，称为 CMBEE，用于生物医学事件提取任务。事件模式的N元组形式用于表示约束信息，将其集成到角色检测和事件类型分类任务中。该框架利用注意力机制和门控机制探索多元组信息的融合，以及局部和全局约束信息融合方法来进一步挖掘事件之间的联系。

结果：

实验结果表明，我们提出的方法在多级事件提取生物医学（MLEE）语料库上取得了最高的 F1 分数，并且在生物医学自然语言处理共享任务 2013 Genia 事件语料库（GE 13）上表现良好。

结论：

实验结果表明，对多事件提取任务的事件模式和约束进行建模对于复杂的生物医学事件提取是有效的。本研究提出的融合策略融合了不同的约束信息，有助于更好地表达语义信息。