AniEE: A Dataset of Animal Experimental Literature for Event Extraction
Dohee Kim, Ra Yoo, Soyoung Yang, Hee Yang, Jaegul Choo
Abstract
Event extraction (EE), as a crucial information extraction (IE) task, aims to identify event triggers and their associated arguments from unstructured text, subsequently classifying them into pre-defined types and roles. In the biomedical domain, EE is widely used to extract complex structures representing biological events from literature. Due to the complicated semantics and specialized domain knowledge, it is challenging to construct biomedical event extraction datasets. Additionally, most existing biomedical EE datasets primarily focus on cell experiments or the overall experimental procedures. Therefore, we introduce AniEE, an event extraction dataset concentrated on the animal experiment stage. We establish a novel animal experiment customized entity and event scheme in collaboration with domain experts. We then create an expert-annotated high-quality dataset containing discontinuous entities and nested events and evaluate our dataset on the recent outstanding NER and EE models.
BibTeX
@inproceedings{
kim2023aniee,
title={Ani{EE}: A Dataset of Animal Experimental Literature for Event Extraction},
author={Dohee Kim and Ra Yoo and Soyoung Yang and Hee Yang and Jaegul Choo},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=BacLV3QUi8}
}