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    <abstract>In this paper, we address Task 1 on adverse drug event (ADE) detection and Task 8 on MultiClinNER at SMM4H-HeaRD 2026. ADE detection is formulated as a multilingual binary classification problem over social media posts spanning German, French, Russian, English, Mandarin and Japanese, with zero-shot on Farsi. Using XLM-RoBERTa-Large with a dual-pooling head, combined with stratified sampling, language-conditioned inputs, translation-based augmentation, and calibrated ensembling, our model achieves a macro F1 score of 0.6088, surpassing both the competition mean (0.5465) and median (0.5798). Our work in MultiClinNER targets clinical NER for English text. Using GLiNER-large with sliding-window inference, query augmentation, and calibrated thresholds, it achieves strict F1 scores of 0.7591 (Disease), 0.7263 (Procedure), and 0.6733 (Symptom), outperforming a PubMedBERT baseline across all entities.</abstract>
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%0 Conference Proceedings
%T Vinland_Vector at #SMM4H-HeaRD 2026: Multilingual ADE Detection and Query-Augmented Clinical NER for English
%A Das, Nirjhar
%A Aich, Rathijit
%A Chowdhury, Mahfuzulhoq
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F das-etal-2026-vinland
%X In this paper, we address Task 1 on adverse drug event (ADE) detection and Task 8 on MultiClinNER at SMM4H-HeaRD 2026. ADE detection is formulated as a multilingual binary classification problem over social media posts spanning German, French, Russian, English, Mandarin and Japanese, with zero-shot on Farsi. Using XLM-RoBERTa-Large with a dual-pooling head, combined with stratified sampling, language-conditioned inputs, translation-based augmentation, and calibrated ensembling, our model achieves a macro F1 score of 0.6088, surpassing both the competition mean (0.5465) and median (0.5798). Our work in MultiClinNER targets clinical NER for English text. Using GLiNER-large with sliding-window inference, query augmentation, and calibrated thresholds, it achieves strict F1 scores of 0.7591 (Disease), 0.7263 (Procedure), and 0.6733 (Symptom), outperforming a PubMedBERT baseline across all entities.
%U https://aclanthology.org/2026.smm4h-1.33/
%P 211-215

Markdown (Informal)

[Vinland_Vector at #SMM4H-HeaRD 2026: Multilingual ADE Detection and Query-Augmented Clinical NER for English](https://aclanthology.org/2026.smm4h-1.33/) (Das et al., SMM4H 2026)

• Vinland_Vector at #SMM4H-HeaRD 2026: Multilingual ADE Detection and Query-Augmented Clinical NER for English (Das et al., SMM4H 2026)

ACL

• Nirjhar Das, Rathijit Aich, and Mahfuzulhoq Chowdhury. 2026. Vinland_Vector at #SMM4H-HeaRD 2026: Multilingual ADE Detection and Query-Augmented Clinical NER for English. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 211–215, San Diego, United States. Association for Computational Linguistics.