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

• URL: http://arxiv.org/abs/2109.12781v1
• Date: Mon, 27 Sep 2021 03:57:17 GMT
• Title: Effective Use of Graph Convolution Network and Contextual Sub-Tree forCommodity News Event Extraction
• Authors: Meisin Lee, Lay-Ki Soon, Eu-Gene Siew
• Abstract summary: 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. Experimental results show the efficiency of the proposed solution, which out-performs existing methods with F1 scores as high as 0.90.
• Score: 1.398696312226463
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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