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Querying across genres for medical claims in news

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

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.

Original languageEnglish
Title of host publicationEMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1783-1789
Number of pages7
ISBN (Electronic)9781952148606
DOIs
StatePublished - 2020
Event2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020 - Virtual, Online
Duration: Nov 16 2020Nov 20 2020

Publication series

NameEMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Conference2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020
CityVirtual, Online
Period11/16/2011/20/20

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