CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction

• Published in: arXiv.org 2022-04
• Main Authors: Lee, Meisin, Lay-Ki Soon, Eu-Gene Siew, Ly Fie Sugianto
• Format: Article
• Language: English
• Subjects: Active learning; Annotations; Crude oil; Data collection; Data mining; Machine learning; News
• ISSN: 2331-8422

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 https://github.com/meisin/CrudeOilNews-Corpus.