We describe SynAnchor, Nasaq’s definition-guided system for AraGenre 2026. The task requires hierarchical Arabic genre classification under disjoint specific-genre inventories across training, development, and test, making generalization to unseen genres a central challenge. SynAnchor addresses this setting by generating Arabic anchor texts from genre definitions using GPT-4o and embedding them with BGE-M3 for similarity-based retrieval. High-confidence cases are classified directly, while ambiguous inputs are selectively adjudicated by GPT-5 among the three highest-ranked candidate genres. On the official test set of 27,972 texts spanning 74 unseen specific genres, SynAnchor achieves a Hierarchical Macro-F1 of 0.6006 on Codabench, compared with 0.2472 for the organizers’ definition-embedding baseline (approximately 2.4). Approximately 25% of test instances are resolved using retrieval alone, without LLM adjudication. The results suggest that combining definition-guided Arabic anchor synthesis with selective LLM reasoning can support hierarchical genre classification under changing label inventories.

We introduce Definition-Guided Learning (DGL), a contrastive training framework that uses dictionary definitions as semantic queries and lemmas as retrieval targets, instantiated here for Arabic reverse dictionary retrieval. We construct a root-stratified Arabic lexical benchmark that limits morphological root overlap between training and test partitions. Zero-shot evaluation suggests a notable gap, with the best pre-trained model achieving only 11.16% Top@1 accuracy on this benchmark. Fine-tuning with DGL yields 41.13% Top@1 and NDCG@10 of 49.32%, an improvement of roughly 3.7 times over the strongest zero-shot baseline we tested. Our results suggest that in-batch negative sampling tends to outperform Triplet Loss in this setting, and that definition length and lexical difficulty appear to trade off in importance across retrieval depth. These findings offer preliminary evidence that lexicographic resources can serve as a useful supervisory signal for Arabic embedding learning, at least within the scope of the benchmark studied here.