# An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking

arXiv: https://arxiv.org/abs/2304.05268 · HTML: https://mlsanity.com/paper/2304.05268/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://mlsanity.com/paper/2106.03794/) (2021)
- [UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://mlsanity.com/paper/1809.01479/) (2018)
- [CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets](https://mlsanity.com/paper/2204.12164/) (2022)
- [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022)
- [Entity-based Claim Representation Improves Fact-Checking of Medical Content in Tweets](https://mlsanity.com/paper/2209.07834/) (2022)
- [PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence](https://mlsanity.com/paper/2207.13970/) (2022)
- [Harnessing Abstractive Summarization for Fact-Checked Claim Detection](https://mlsanity.com/paper/2209.04612/) (2022)
- [Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)](https://mlsanity.com/paper/2204.09952/) (2022)

## Built on by (most cited first, 4 of 4)

- [The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval](https://mlsanity.com/paper/2503.14828/) (2025)
- [What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification](https://mlsanity.com/paper/2402.01360/) (2024)
- [A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models](https://mlsanity.com/paper/2410.21360/) (2024)
- [Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability](https://mlsanity.com/paper/2412.11653/) (2024)

## Abstract (arXiv)

Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to extract concise claims based on medical entities in the text. However, their study has two limitations: First, it relies on gold-annotated entities. Therefore, its feasibility for a real-world application cannot be assessed since this requires detecting relevant entities automatically. Second, they represent claim entities with the original tokens. This constitutes a terminology mismatch which potentially limits the fact-checking performance. To understand both challenges, we propose a claim extraction pipeline for medical tweets that incorporates named entity recognition and terminology normalization via entity linking. We show that automatic NER does lead to a performance drop in comparison to using gold annotations but the fact-checking performance still improves considerably over inputting the unchanged tweets. Normalizing entities to their canonical forms does, however, not improve the performance.