Cross-topic stance detection in Arabic is the task of identifying whether a text expresses a pro, against, or neutral position toward a given issue, and it is particularly challenging under topic shifts and class imbalance. In Subtask B of the StanceNakba 2026 shared task on Arabic cross-topic stance detection, we are given a Levantine Arabic sentence and one of two topics: “Normalization with Israel” or “Refugee/Immigrant Presence in Jordan,” and we must classify the expressed stance. A central difficulty is the systematic failure of standard fine-tuning to recognize the minority neutral class, driven by majority-class dominance in cross-entropy training and accuracy-based checkpoint selection. To address this, we combine random oversampling with class-weighted cross-entropy loss, and we build an ensemble of four Arabic pre-trained transformers MARBERT, AraBERT Large, XLM-RoBERTa Base, and CAMeL-BERT Mix each trained using Stratified 5-Fold cross-validation. Our final system achieves a macro-F1 of 0.9777 and an accuracy of 97.79% on the evaluation set.

Lexicons are indispensable tools for textual analysis through labelled term associations.Despite their utility, lexicons are static and require manual effort to curate and maintain.Regular updates are essential to stay relevant amid semantic shifts and neologisms during language evolution.In this work, we explore the potential of supervised learning for expanding lexicons using dictionaries.We study the effect of using dictionaries with varying properties such as noise, size, labels, structure and curation method.Definitions are used as input features to a transformer model (BERT) that assigns categories to terms.We analyse the expansions using varying English dictionaries and lexicons for estimated accuracy, coverage and labelling consistency and apply the expanded versions to a downstream task.Our analyses show dictionary based expansion is a robust approach.We release our expanded lexicons, code, and pretrained models.