Name
Abstract | ||
|---|---|---|
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system that combines biomedical text mining and QA techniques to provide answers to questions in real-time. Our system leverages both supervised and unsupervised approaches to provide informative answers using DenSPI (Seo et al., 2019) and BEST (Lee et al., 2016). Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on facts about COVID-19. We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19 but for future pandemics as well. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.18653/v1/2020.nlpcovid19-2.1 | NLP4COVID@EMNLP |
DocType | Volume | Citations |
Conference | 2020.nlpcovid19-2 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinhyuk Lee | 1 | 0 | 1.01 |
| Sean S. Yi | 2 | 0 | 0.34 |
| Minbyul Jeong | 3 | 4 | 2.11 |
| Mujeen Sung | 4 | 4 | 2.11 |
| Wonjin Yoon | 5 | 4 | 2.11 |
| Yonghwa Choi | 6 | 11 | 2.32 |
| Miyoung Ko | 7 | 6 | 2.18 |
| Jaewoo Kang | 8 | 1258 | 179.45 |