  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.

Computation and Language