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

Autor:	Schlicht, Ipek Baris, de Paula, Angel Felipe Magnossão, Rosso, Paolo
Rok vydání:	2021
Předmět:	Computer Science - Information Retrieval Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages; presented at the TREC 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.06080 Zobrazit plný text záznamu View this record from Arxiv