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

arXiv: https://arxiv.org/abs/2112.06080 · HTML: https://mlsanity.com/paper/2112.06080/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 11 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Evaluation Measures for Relevance and Credibility in Ranked Lists](https://mlsanity.com/paper/1708.07157/) (2017)
- [Automatically Assessing Quality of Online Health Articles](https://mlsanity.com/paper/2004.05113/) (2020)
- [University of Copenhagen Participation in TREC Health Misinformation Track 2020](https://mlsanity.com/paper/2103.02462/) (2021)

## Built on by (most cited first, 2 of 2)

- [Enhancing Documents with Multidimensional Relevance Statements in Cross-encoder Re-ranking](https://mlsanity.com/paper/2306.10979/) (2023)
- [AI-UPV at EXIST 2023 -- Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime](https://mlsanity.com/paper/2307.03385/) (2023)

## Abstract (arXiv)

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.