@inproceedings{lee-etal-2021-effective,
title = "Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction",
author = "Lee, Meisin and
Soon, Lay-Ki and
Siew, Eu-Gene",
editor = "Hahn, Udo and
Hoste, Veronique and
Stent, Amanda",
booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.econlp-1.10",
doi = "10.18653/v1/2021.econlp-1.10",
pages = "69--81",
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 available.",
}
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<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 available.</abstract>
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%0 Conference Proceedings
%T Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction
%A Lee, Meisin
%A Soon, Lay-Ki
%A Siew, Eu-Gene
%Y Hahn, Udo
%Y Hoste, Veronique
%Y Stent, Amanda
%S Proceedings of the Third Workshop on Economics and Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F lee-etal-2021-effective
%X 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 available.
%R 10.18653/v1/2021.econlp-1.10
%U https://aclanthology.org/2021.econlp-1.10
%U https://doi.org/10.18653/v1/2021.econlp-1.10
%P 69-81
Markdown (Informal)
[Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction](https://aclanthology.org/2021.econlp-1.10) (Lee et al., ECONLP 2021)
ACL