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(BEE)领域已有许多模型被提出。其中部分模型采用最短依存路径(SDP)信息来表征论元分类任务。然而这种表征方式存在缺陷，因为即使依存解析图中缺失一个词，也可能完全改变最终预测结果。为此，本研究采用依存图的完整邻接矩阵，通过图卷积网络(GCN)对单个词元进行嵌入表示。同时通过消融实验验证了依存图对整体性能的影响。实验结果表明，使用依存图信息能带来显著性能提升。所提出的模型在不同数据集上的BEE任务中均略微优于当前最先进的模型。