In recent years, biomedical event extraction has been dominated by complicated pipeline and joint methods, which need to be simplified. In addition, existing work has not effectively utilized trigger word information explicitly. Hence, we propose MLSL, a method based on multi-layer sequence labeling for joint biomedical event extraction. MLSL does not introduce prior knowledge and complex structures. Moreover, it explicitly incorporates the information of candidate trigger words into the sequence labeling to learn the interaction relationships between trigger words and argument roles. Based on this, MLSL can learn well with just a simple workflow. Extensive experimentation demonstrates the superiority of MLSL in terms of extraction performance compared to other state-of-the-art methods.

翻译：近年来，生物医学事件抽取领域主要被复杂的流水线方法和联合方法所主导，这些方法亟需简化。此外，现有工作未能有效显式地利用触发词信息。为此，我们提出了MLSL，一种基于多层序列标注的联合生物医学事件抽取方法。MLSL无需引入先验知识和复杂结构。更重要的是，它将候选触发词的信息显式地整合到序列标注中，以学习触发词与论元角色之间的交互关系。基于此，MLSL仅需简单的工作流程即可实现良好的学习效果。大量实验表明，与其他最先进的方法相比，MLSL在抽取性能方面具有优越性。