School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning, China.
Information and Communication Engineering Postdoctoral Research Station, Dalian University of Technology, Dalian, Liaoning, China.
BMC Bioinformatics. 2022 Jul 29;23(1):308. doi: 10.1186/s12859-022-04854-0.
Biomedical event extraction is a fundamental task in biomedical text mining, which provides inspiration for medicine research and disease prevention. Biomedical events include simple events and complex events. Existing biomedical event extraction methods usually deal with simple events and complex events uniformly, and the performance of complex event extraction is relatively low.
In this paper, we propose a fine-grained Bidirectional Long Short Term Memory method for biomedical event extraction, which designs different argument detection models for simple and complex events respectively. In addition, multi-level attention is designed to improve the performance of complex event extraction, and sentence embeddings are integrated to obtain sentence level information which can resolve the ambiguities for some types of events. Our method achieves state-of-the-art performance on the commonly used dataset Multi-Level Event Extraction.
The sentence embeddings enrich the global sentence-level information. The fine-grained argument detection model improves the performance of complex biomedical event extraction. Furthermore, the multi-level attention mechanism enhances the interactions among relevant arguments. The experimental results demonstrate the effectiveness of the proposed method for biomedical event extraction.
生物医学事件抽取是生物医学文本挖掘中的一项基本任务，为医学研究和疾病预防提供了启示。生物医学事件包括简单事件和复杂事件。现有的生物医学事件抽取方法通常统一处理简单事件和复杂事件，复杂事件抽取的性能相对较低。
在本文中，我们提出了一种用于生物医学事件抽取的细粒度双向长短期记忆方法，为简单事件和复杂事件分别设计了不同的参数检测模型。此外，设计了多层次注意力机制来提高复杂事件抽取的性能，并集成句子嵌入以获取句子级信息，从而解决某些类型事件的歧义问题。我们的方法在常用的多层次事件抽取数据集上达到了最先进的性能。
句子嵌入丰富了全局句子级信息。细粒度参数检测模型提高了复杂生物医学事件抽取的性能。此外，多层次注意力机制增强了相关参数之间的相互作用。实验结果表明，该方法在生物医学事件抽取中是有效的。
