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UPV at TREC Health Misinformation Track 2021 Ranking with SBERT and Quality Estimators

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arxiv 2112.06080 v1 pith:FJOYXYNN submitted 2021-12-11 cs.IR cs.AI

classification cs.IRcs.AI
keywords healthmisinformationtrackdocumentsproblemqualitysearchtrec
verification ladder T0 review T1 audit T2 compute T3 formal
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Health misinformation on search engines is a significant problem that could negatively affect individuals or public health. To mitigate the problem, TREC organizes a health misinformation track. This paper presents our submissions to this track. We use a BM25 and a domain-specific semantic search engine for retrieving initial documents. Later, we examine a health news schema for quality assessment and apply it to re-rank documents. We merge the scores from the different components by using reciprocal rank fusion. Finally, we discuss the results and conclude with future works.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy

    cs.IR 2025-02 conditional novelty 5.0 of 10

    A three-stage RAG pipeline generates a cited summary (GenText) from PubMed Central passages and ranks health documents by topical relevance plus alignment with that summary, outperforming baselines on CLEF eHealth and...

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