Clinical language models are typically pretrained with self-supervised objectives whose geometry reflects linguistic co-occurrence rather than clinical knowledge structure. For downstream tasks that operate directly on the representation space, without task-specific fine-tuning, this gap limits what the model can do. We introduce DOKTERBERT (Dutch Ontology-grounded Knowledge-injected Text Encoder for Representations using BERT), a Dutch clinical language model pre-trained with a structure-aware contrastive objective that aligns contextual span representations to SNOMED concept anchors, with negative pressure weighted by graph distance in the SNOMED hierarchy. We evaluate DOKTERBERT against three Dutch baselines (RobBERT, MedRoBERTa.nl, and MedRoBERTa.nl-SapBERT) through supervised named entity recognition on MultiClinNER-nl and an unsupervised representation analysis spanning retrieval, clustering, entity linking, and concept-level separation. On supervised NER, all four models perform comparably; on the representational evaluations, DOKTERBERT separates from every baseline. Standard fine-tuning evaluation obscures pre-training-level differences in representation quality that representation analysis exposes, and these differences matter for clinical applications that depend on embedding geometry.

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.