Semantic retrieval of Uzbek seismic safety regulations

This study presents a semantic retrieval approach for Uzbek seismic safety regulations and engineering documents. The proposed system combines paragraph-level indexing with two retrieval models, a classical TF-IDF baseline and a FastText-based subword embedding model, to improve access to relevant regulatory and technical text units in a morphologically rich language environment. A specialized corpus was compiled from seismic engineering and safety documentation and segmented into paragraphs for fine-grained semantic search. The retrieval performance was evaluated on 100 domain-specific queries using Precision@5, Recall@5, Mean Average Precision (MAP), and Mean Reciprocal Rank (MRR). The experimental results show that the FastText model outperformed TF-IDF across all major metrics, achieving Precision@5 = 0.7444 and Recall@5 = 0.6875, compared with 0.6017 and 0.5418, respectively. The observed improvement was statistically significant according to paired t-test analysis ( t = 15.1372, p < 0.001). The findings indicate that subword-based semantic modeling improves retrieval quality for Uzbek seismic safety documentation and can support faster access to relevant engineering regulations and compliance information.