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

TL;DR

This paper introduces a novel event extraction method for commodity news using Graph Convolutional Networks and a domain-adapted BERT model, significantly improving accuracy over existing approaches.

Contribution

It proposes combining GCN with a pruned dependency parse tree and a domain-specific BERT model for enhanced commodity news event extraction.

Findings

Achieved F1 scores up to 0.90, outperforming existing methods.

Pre-trained ComBERT outperforms GloVe by 23% and BERT/RoBERTa by 7% in argument role classification.

Demonstrated the effectiveness of the proposed approach through experimental results.

Abstract

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.