Document Classification for COVID-19 Literature
Bernal Jiménez Gutiérrez, Juncheng Zeng, Dongdong Zhang, Ping Zhang, Yu Su
Abstract
The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset. We find that pre-trained language models outperform other models in both low and high data regimes, achieving a maximum F1 score of around 86%. We note that even the highest performing models still struggle with label correlation, distraction from introductory text and CORD-19 generalization. Both data and code are available on GitHub.- Anthology ID:
- 2020.nlpcovid19-acl.3
- Volume:
- Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Karin Verspoor, Kevin Bretonnel Cohen, Mark Dredze, Emilio Ferrara, Jonathan May, Robert Munro, Cecile Paris, Byron Wallace
- Venue:
- NLP-COVID19
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2020.nlpcovid19-acl.3
- DOI:
- Cite (ACL):
- Bernal Jiménez Gutiérrez, Juncheng Zeng, Dongdong Zhang, Ping Zhang, and Yu Su. 2020. Document Classification for COVID-19 Literature. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
- Cite (Informal):
- Document Classification for COVID-19 Literature (Jiménez Gutiérrez et al., NLP-COVID19 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.nlpcovid19-acl.3.pdf