Event extraction in commodity news is a less researched area as compared to generic eventextraction. However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning event-eventrelations, which can then be used for commodity price prediction. The events foundin 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 extraction in commodity news. The event extraction model is trained using feature embeddings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commoditynews corpus. Experimental results show the efficiency of the proposed solution, which out-performs existing methods with F1 scores ashigh as 0.90. Furthermore, our pre-trained language model outperforms GloVe by 23%, andBERT and RoBERTa by 7% in terms of argu-ment roles classification. For the goal of re-producibility, the code and trained models aremade publicly available.

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



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W1: 3rd Workshop on Economics and Natural Language Processing (ECONLP)

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Meisin Lee

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

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