Abstract
We present a query-based biomedical information retrieval task across two vastly different genres – newswire and research literature – where the goal is to find the research publication that supports the primary claim made in a health-related news article. For this task, we present a new dataset of 5,034 claims from news paired with research abstracts. Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list. We compare the classical IR approach using BM25 with more recent transformer-based models. Our results show that cross-genre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.- Anthology ID:
- 2020.emnlp-main.139
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1783–1789
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.139
- DOI:
- 10.18653/v1/2020.emnlp-main.139
- Cite (ACL):
- Chaoyuan Zuo, Narayan Acharya, and Ritwik Banerjee. 2020. Querying Across Genres for Medical Claims in News. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1783–1789, Online. Association for Computational Linguistics.
- Cite (Informal):
- Querying Across Genres for Medical Claims in News (Zuo et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.emnlp-main.139.pdf