用图神经网络捕捉句法信息，提升生物医学事件抽取准确率
Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks
- 用依赖图全邻接矩阵替代最短路径，增强句法信息表达
- 在多个数据集上显著优于传统方法，性能接近顶尖水平
- 适合关注句法建模的自然语言处理研究者和医疗文本分析场景
生物医学事件抽取（BEE）已有多种模型被提出。部分模型采用最短依赖路径（SDP）表示论元分类任务，但该表示存在缺陷：依赖图中缺失任一词语可能导致最终预测完全改变。为此，本文使用依赖图的完整邻接矩阵，通过图卷积网络（GCN）对每个词元进行嵌入表示。消融实验验证了依赖图信息对整体性能的影响。结果表明，引入依赖图信息后性能显著提升。所提模型在多个数据集上的表现略超现有最优模型。
原文摘要 · Abstract (English)
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.
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