ADENER: A syntax-augmented grid-tagging model for Adverse Drug Event extraction in social media.

Objective: Adverse Drug Event (ADE) extraction from social media is a critical yet challenging task due to the semantic similarity between adverse effects and therapeutic indications, as well as the prevalence of overlapping and discontinuous mentions often caused by comorbid conditions. This study aims to develop a robust model for accurate ADE extraction from noisy and irregular social media texts.

Methods: We propose ADENER, a grid-tagging architecture that models ADE extraction as multi-label word-pair classification. ADENER incorporates two core encoding mechanisms: the convolutional capture layer fuses multi-dimensional textual features, captures long-range word-pair dependencies via dilated convolutions, and enhances interactions through semantic association matrices for social media text irregularities; the syntactic affine layer integrates path-level dependency information to enhance global logic understanding, enabling the model to distinguish between therapeutic symptom entities and ADE entities through syntactic cues. The decoding stage uses four-type relational labels to uniformly decode flat, overlapping, and discontinuous ADE mentions.

Results: We evaluated ADENER on three widely used ADE extraction datasets: CADEC, CADECv2, SMM4H. The model achieved F1 scores of 74.64%, 77.97%, 61.73% on these datasets, respectively, outperforming all compared baseline models while maintaining competitive computational efficiency. The results demonstrate the effectiveness of our model in addressing the challenges posed by irregular and noisy social media data.

Conclusion: ADENER offers a unified and effective solution for ADE extraction from social media, capable of handling flat, overlapping, and discontinuous entity mentions and correctly distinguishing ADE entities from therapeutic symptom entities. By incorporating convolutional capture layers for semantic word-pair interactions and syntactic affine layers for dependency-based logic understanding, our approach significantly improves extraction accuracy, providing a valuable tool for pharmacovigilance research and real-world drug safety monitoring.