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Title: Querying Across Genres for Medical Claims in News
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.  more » « less
Award ID(s):
1834597
NSF-PAR ID:
10233316
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Page Range / eLocation ID:
1783 to 1789
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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