论文标题
生物医学事件提取带有分层知识图
Biomedical Event Extraction with Hierarchical Knowledge Graphs
论文作者
论文摘要
生物医学事件提取对于理解科学语料库中描述的生物分子相互作用至关重要。主要挑战之一是识别与非指导性触发单词相关的嵌套结构化事件。我们建议将统一医学语言系统（UMLS）的领域知识通过图形边缘条件的注意网络（Geanet）和分层图表示，将领域知识从统一的医学语言系统（UMLS）纳入预训练的语言模型。为了更好地识别触发单词，首先根据UMLS的共同建模的层次知识图将每个句子都基于句子图。然后，Geanet将接地的图传播，Geanet是一种新型的图神经网络，可增强推断复杂事件的功能。在BIONLP 2011 GENIA事件提取任务上，我们的方法分别在所有事件和复杂事件方面取得了1.41％的F1和3.19％的F1。消融研究证实了Geanet和等级KG的重要性。
Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.
