Many models are proposed in the literature on biomedical event extraction(BEE). Some of them use the shortest dependency path(SDP) information to represent the argument classification task. There is an issue with this representation since even missing one word from the dependency parsing graph may totally change the final prediction. To this end, the full adjacency matrix of the dependency graph is used to embed individual tokens using a graph convolutional network(GCN). An ablation study is also done to show the effect of the dependency graph on the overall performance. The results show a significant improvement when dependency graph information is used. The proposed model slightly outperforms state-of-the-art models on BEE over different datasets.

翻译：生物医学事件抽取领域已提出多种模型。部分模型采用最短依存路径信息来表征论元分类任务，但该表征方式存在缺陷，因为即使缺失依存解析图中的单个词汇，也可能完全改变最终预测结果。为此，本研究通过图卷积网络利用依存图的完整邻接矩阵对独立词汇进行嵌入表示。消融实验进一步验证了依存图对整体性能的影响。结果表明，使用依存图信息可带来显著性能提升。所提模型在不同数据集上的生物医学事件抽取任务中均略优于当前最优模型。