We present our solution to Subtask 1 of the Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages. Our approach integrates fine-tuned multilingual transformer models with two complementary robustness-oriented strategies: Walking Embeddings and Content-Debiasing. With the first, we tried to understand the change in embeddings when various manipulation techniques were applied. The latter leverages a supervised contrastive objective over semantically equivalent yet stylistically divergent text pairs, generated via GPT-4. We conduct extensive experiments, including 5-fold cross-validation and out-of-domain evaluation, and explore the impact of contrastive loss weighting.

We present a system for the SlavicNLP 2025 Shared Task on multilabel classification of 25 persuasion techniques across Slavic languages. We investigate the effectiveness of in-context learning with one-shot classification, automatic prompt refinement, and supervised fine-tuning using self-generated annotations. Our findings highlight the potential of LLM-based system to generalize across languages and label sets with minimal supervision.

This paper reports on an endeavour of creating basic linguistic resources for geo-referencing of Polish free-text documents. We have defined a fine-grained named entity hierarchy, produced an exhaustive gazetteer, and developed named-entity grammars for Polish. Additionally, an annotated corpus for the cadastral domain was prepared for evaluation purposes. Our baseline approach to geo-referencing is based on application of aforementioned resources and a lightweight co-referencing technique which utilizes string-similarity metric of Jaro-Winkler. We carried out a detailed evaluation of detecting locations, organizations and persons, which revealed that best results are obtained via application of a combined grammar for all types. The application of lightweight co-referencing for organizations and persons improves recall but deteriorates precision, and no gain is observed for locations. The paper is accompanied by a demo, a geo-referencing application capable of: (a) finding documents and text fragments based on named entities and (b) populating the spatial ontology from texts.