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Event extraction in commodity news is a less researched area as compared togeneric event extraction. However, accurate event extraction from commoditynews is useful in abroad range of applications such as under-standing eventchains and learning event-event relations, which can then be used for commodityprice prediction. The events found in commodity news exhibit characteristicsdifferent from generic events, hence posing a unique challenge in eventextraction using existing methods. This paper proposes an effective use ofGraph Convolutional Networks(GCN) with a pruned dependency parse tree, termedcontextual sub-tree, for better event ex-traction in commodity news. The eventex-traction model is trained using feature embed-dings from ComBERT, aBERT-based masked language model that was produced through domain-adaptivepre-training on a commodity news corpus. Experimental results show theefficiency of the proposed solution, which out-performs existing methods withF1 scores as high as 0.90. Furthermore, our pre-trained language modeloutperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument rolesclassification. For the goal of re-producibility, the code and trained modelsare made publicly available1.

Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event Extraction