In this paper, we present CrudeOilNews, a corpus of English Crude Oil news
for event extraction. It is the first of its kind for Commodity News and serve
to contribute towards resource building for economic and financial text mining.
This paper describes the data collection process, the annotation methodology
and the event typology used in producing the corpus. Firstly, a seed set of 175
news articles were manually annotated, of which a subset of 25 news were used
as the adjudicated reference test set for inter-annotator and system
evaluation. Agreement was generally substantial and annotator performance was
adequate, indicating that the annotation scheme produces consistent event
annotations of high quality. Subsequently the dataset is expanded through (1)
data augmentation and (2) Human-in-the-loop active learning. The resulting
corpus has 425 news articles with approximately 11k events annotated. As part
of active learning process, the corpus was used to train basic event extraction
models for machine labeling, the resulting models also serve as a validation or
as a pilot study demonstrating the use of the corpus in machine learning
purposes. The annotated corpus is made available for academic research purpose
at this https URL.

本文介绍了 CrudeOilNews 的数据收集过程、标注方法学和事件类型学，旨在为经济和金融文本挖掘提供贡献性资源，通过种子数据扩充和人类协商来拓展数据集，在机器学习目的方面，训练了基本的事件提取模型。

CrudeOilNews: 带事件抽取的原油新闻注释语料库

CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction

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.

本文介绍了一种使用图卷积网络 (GCN) 和修剪的依赖关系解析树来更好地从商品新闻中提取事件的方法。该方法采用基于 ComBERT 的特征嵌入来训练事件提取模型，实验结果表明，该方法的效率比现有方法高，F1 得分达到 0.90。此外，我们的预训练语言模型在论元角色分类方面优于 GloVe 23％，优于 BERT 和 RoBERTa 7％。