# A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification

arXiv: https://arxiv.org/abs/2011.00057 · HTML: https://mlsanity.com/paper/2011.00057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

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

- [A New Entity Extraction Method Based on Machine Reading Comprehension](https://mlsanity.com/paper/2108.06444/) (2021)
- [Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study](https://mlsanity.com/paper/2408.13501/) (2024)

## Abstract (arXiv)

Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score.