# Increasing Adverse Drug Events extraction robustness on social media: case study on negation and speculation

arXiv: https://arxiv.org/abs/2209.02812 · HTML: https://mlsanity.com/paper/2209.02812/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [DynaSent: A Dynamic Benchmark for Sentiment Analysis](https://mlsanity.com/paper/2012.15349/) (2020)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies](https://mlsanity.com/paper/2105.08882/) (2021)
- [NADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations](https://mlsanity.com/paper/2109.10080/) (2021)

## Built on by (most cited first, 3 of 3)

- [Generalizing over Long Tail Concepts for Medical Term Normalization](https://mlsanity.com/paper/2210.11947/) (2022)
- [Extensive Evaluation of Transformer-based Architectures for Adverse Drug Events Extraction](https://mlsanity.com/paper/2306.05276/) (2023)
- [A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages](https://mlsanity.com/paper/2403.18336/) (2024)

## Abstract (arXiv)

In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums. Given the large volume of reports, pharmacovigilance has focused on ways to use Natural Language Processing (NLP) techniques to rapidly examine these large collections of text, detecting mentions of drug-related adverse reactions to trigger medical investigations. However, despite the growing interest in the task and the advances in NLP, the robustness of these models in face of linguistic phenomena such as negations and speculations is an open research question. Negations and speculations are pervasive phenomena in natural language, and can severely hamper the ability of an automated system to discriminate between factual and nonfactual statements in text. In this paper we take into consideration four state-of-the-art systems for ADE detection on social media texts. We introduce SNAX, a benchmark to test their performance against samples containing negated and speculated ADEs, showing their fragility against these phenomena. We then introduce two possible strategies to increase the robustness of these models, showing that both of them bring significant increases in performance, lowering the number of spurious entities predicted by the models by 60% for negation and 80% for speculations.