We describe our submission to SMM4H-HeaRD 2026 Task 7, which asks systems tolabel ClinicalImpacts and SocialImpactsspans in Reddit posts about non-medical sub-stance use. We compare four pipeline shapesbuilt on the same DeBERTa-v3-base back-bone: (i) a direct 5-class encoder with a linear-chain CRF head, (ii) a two-stage detect-then-classify pipeline that delegates span typingto an instruction-tuned LLM (Qwen2.5-7Bor Gemma-3-12B, 4-bit NF4), (iii) an auditpipeline in which the same LLM verifies theencoder’s predictions, and (iv) a classical-MLvariant that replaces the LLM with an SVMtrained on encoder span embeddings. Across16 configurations, the encoder-only DeBERTa-v3 + CRF configuration is the strongest sin-gle system on the official test split, reaching45.4% strict and 54.2% relaxed F1 — +8.6/ +5.3 points above a mental-roberta-basebaseline. LLM audits give a small dev gain thatdoes not transfer to test.

This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We developed various datageneration techniques that expand the originally provided train set and show that all methods increase the performance of modelstrained on these expanded datasets. Our final system outperformed the baseline system from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline’s 67.4.

With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. Addressing the issue requires solving a number of challenging problems such as identifying messages containing claims, determining their check-worthiness and factuality, and their potential to do harm as well as the nature of that harm, to mention just a few. To address this gap, we release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis that (i) focuses on COVID-19, (ii) combines the perspectives and the interests of journalists, fact-checkers, social media platforms, policy makers, and society, and (iii) covers Arabic, Bulgarian, Dutch, and English. Finally, we show strong evaluation results using pretrained Transformers, thus confirming the practical utility of the dataset in monolingual vs. multilingual, and single task vs. multitask settings.