@inproceedings{lee-etal-2022-crudeoilnews,
title = "{C}rude{O}il{N}ews: An Annotated Crude Oil News Corpus for Event Extraction",
author = "Lee, Meisin and
Soon, Lay-Ki and
Siew, Eu Gene and
Sugianto, Ly Fie",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.49",
pages = "465--479",
abstract = "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 serves 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 was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator 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 the 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 \url{https://github.com/meisin/CrudeOilNews-Corpus}",
}
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%0 Conference Proceedings
%T CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction
%A Lee, Meisin
%A Soon, Lay-Ki
%A Siew, Eu Gene
%A Sugianto, Ly Fie
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F lee-etal-2022-crudeoilnews
%X 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 serves 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 was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator 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 the 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
%U https://aclanthology.org/2022.lrec-1.49
%P 465-479
Markdown (Informal)
[CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction](https://aclanthology.org/2022.lrec-1.49) (Lee et al., LREC 2022)
ACL