Abstract
Understanding scientific articles related to COVID-19 requires broad knowledge about concepts such as symptoms, diseases and medicine. Given the very large and ever-growing scientific articles related to COVID-19, it is a daunting task even for experts to recognize the large set of concepts mentioned in these articles. In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles. We develop an approach to curate a COVID-19 concept wikification dataset by mining Wikipedia text and the associated intra-Wikipedia links. We also develop an end-to-end system for concept wikification for COVID-19. Preliminary experiments show very encouraging results. Our dataset, code and pre-trained model are available at github.com/panlybero/Covid19_wikification.- Anthology ID:
- 2020.nlpcovid19-2.29
- Volume:
- Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
- Month:
- December
- Year:
- 2020
- Address:
- Online
- Venue:
- NLP-COVID19
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2020.nlpcovid19-2.29
- DOI:
- 10.18653/v1/2020.nlpcovid19-2.29
- Cite (ACL):
- Panagiotis Lymperopoulos, Haoling Qiu, and Bonan Min. 2020. Concept Wikification for COVID-19. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.
- Cite (Informal):
- Concept Wikification for COVID-19 (Lymperopoulos et al., NLP-COVID19 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.nlpcovid19-2.29.pdf
- Code
- panlybero/covid19_wikification