Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning event-event relations, which can then be used for commodity price prediction. The events found in commodity news exhibit characteristics different from generic events, hence posing a unique challenge in event extraction using existing methods. This paper proposes an effective use of Graph Convolutional Networks(GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event ex-traction in commodity news. The event ex-traction model is trained using feature embed-dings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commodity news corpus. Experimental results show the efficiency of the proposed solution, which out-performs existing methods with F1 scores as high as 0.90. Furthermore, our pre-trained language model outperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument roles classification. For the goal of re-producibility, the code and trained models are made publicly available1.

本文介绍了一种使用图卷积网络(GCN)和修剪的依赖关系解析树来更好地从商品新闻中提取事件的方法。该方法采用基于ComBERT的特征嵌入来训练事件提取模型，实验结果表明，该方法的效率比现有方法高，F1得分达到0.90。此外，我们的预训练语言模型在论元角色分类方面优于GloVe 23％，优于BERT和RoBERTa 7％。

利用图卷积网络和上下文子树有效地进行商品新闻事件抽取