InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research
Claire Bonial, Stephanie M. Lukin, David Doughty, Steven Hill, Clare Voss
Abstract
This paper examines how Abstract Meaning Representation (AMR) can be utilized for finding answers to research questions in medical scientific documents, in particular, to advance the study of UV (ultraviolet) inactivation of the novel coronavirus that causes the disease COVID-19. We describe the development of a proof-of-concept prototype tool, InfoForager, which uses AMR to conduct a semantic search, targeting the meaning of the user question, and matching this to sentences in medical documents that may contain information to answer that question. This work was conducted as a sprint over a period of six weeks, and reveals both promising results and challenges in reducing the user search time relating to COVID-19 research, and in general, domain adaption of AMR for this task.- Anthology ID:
- 2020.dmr-1.7
- Volume:
- Proceedings of the Second International Workshop on Designing Meaning Representations
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona Spain (online)
- Venue:
- DMR
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 67–77
- Language:
- URL:
- https://aclanthology.org/2020.dmr-1.7
- DOI:
- Cite (ACL):
- Claire Bonial, Stephanie M. Lukin, David Doughty, Steven Hill, and Clare Voss. 2020. InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research. In Proceedings of the Second International Workshop on Designing Meaning Representations, pages 67–77, Barcelona Spain (online). Association for Computational Linguistics.
- Cite (Informal):
- InfoForager: Leveraging Semantic Search with AMR for COVID-19 Research (Bonial et al., DMR 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.dmr-1.7.pdf
- Data
- Bio