SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel Weld, Marti Hearst, Jevin West
Abstract
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions. Search engines are designed for targeted queries, not for discovery of connections across a corpus. In this paper, we present SciSight, a system for exploratory search of COVID-19 research integrating two key capabilities: first, exploring associations between biomedical facets automatically extracted from papers (e.g., genes, drugs, diseases, patient outcomes); second, combining textual and network information to search and visualize groups of researchers and their ties. SciSight has so far served over 15K users with over 42K page views and 13% returns.- Anthology ID:
- 2020.emnlp-demos.18
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- October
- Year:
- 2020
- Address:
- Online
- Editors:
- Qun Liu, David Schlangen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 135–143
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-demos.18
- DOI:
- 10.18653/v1/2020.emnlp-demos.18
- Cite (ACL):
- Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel Weld, Marti Hearst, and Jevin West. 2020. SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 135–143, Online. Association for Computational Linguistics.
- Cite (Informal):
- SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search (Hope et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.emnlp-demos.18.pdf
- Data
- CORD-19