We present an approach for medical text coding with SNOMED CT. Our approach uses publicly available linked open data from terminologies and ontologies as training data for the algorithms. We claim that even small training corpora made of short text snippets can be used to train models for the given task. We propose a method based on transformers enhanced with clustering and filtering of the candidates. Further, we adopt a classical machine learning approach - support vector classification (SVC) using transformer embeddings. The resulting approach proves to be more accurate than the predictions given by Large Language Models. We evaluate on a dataset generated from linked open data for SNOMED codes related to morphology and topography for four use cases. Our transformers-based approach achieves an F1-score of 0.82 for morphology and 0.99 for topography codes. Further, we validate the applicability of our approach in a clinical context using labelled real clinical data that are not used for model training.

Event extraction from textual data is a NLP research task relevant to a plethora of domains. Most approaches aim to recognize events from a predefined event schema, consisting of event types and their corresponding arguments. For domains, such as disinformation, where new event types emerge frequently, there is a need to adapt such fixed event schemas to accommodate for new event types. We present NEXT (New Event eXTraction) - a resource-sparse approach to extending a close-domain model to novel event types, that requires a very small number of annotated samples for fine-tuning performed on a single GPU. Furthermore, our results suggest that this approach is suitable not only for extraction of new event types, but also for recognition of existing event types, as the use of this approach on a new dataset leads to improved recall for all existing events while retaining precision.