Abstract
The paper presents a framework for bilingual terminology extraction targeting highly technical domains and the low-resource Kazakh language. We develop two unsupervised term annotators and introduce a hybrid Semantic NMF model that combines non-negative matrix factorization with multilingual embeddings (LaBSE, E5-base, BGE-M3). We further release Matcha, a BIO-annotated English and Kazakh corpus (~3k terms) to fine-tune supervised extractors (BERT-BiLSTM-CRF, XLM-RoBERTa). Across ACTER and ACL RD-TEC 2.0 benchmarks and the new Matcha corpus, UA2 attains up to 51.8% F1, approaching supervised performance; supervised models reach about 70% F1 in Materials Science. The results highlight strong cross-lingual/cross-domain transfer for Semantic-NMF-based unsupervised methods and demonstrate a scalable path to terminology mining and downstream knowledge graph construction in under-represented languages.