20.500.12556/RUL-179419 Recent advances in automatic term extraction a comprehensive survey Automatic terminology or term extraction (ATE) is a Natural Language Processing (NLP) task intended to automatically identify specialized terms present in domain-specific corpora. As units of knowledge in a speciic ield of expertise, extracted terms are not only beneicial for several terminographical tasks, but also support and improve several complex downstream tasks, e.g., information retrieval, machine translation, topic detection, and sentiment analysis. ATE systems and datasets annotated for the task at hand have been studied and developed for decades, but more recent approaches have increasingly involved novel neural systems. Despite a large amount of new research on ATE tasks, systematic survey studies covering novel neural approaches are lacking, especially when it comes to the usage of large-scale language models (LLMs). We present a comprehensive survey of neural approaches to ATE, focusing on transformer-based neural models and the recent generative approaches based on LLMs. The study also compares these systems and previous ML-based approaches, which employed feature engineering and non-neural supervised learning algorithms. computing methodologies natural language processing neural networks language resources language models transformers automatic term extraction ATE low-resourced languages monolingual multilingual deep learning zero-shot few-shot transfer learning prompt engineering large-scale language models LLMs računalniške metodologije obdelava naravnega jezika nevronske mreže jezikovni viri jezikovni modeli transformatorji avtomatsko pridobivanje izrazov ATE jeziki z malo viri enojezični večjezični globoko učenje učenje z ničelnim številom poskusov učenje z malo poskusi prenosno učenje hitro inženirstvo jezikovni modeli velikega obsega true false true Angleški jezik Slovenski jezik Članek v reviji 2026-02-13 08:53:22 2026-02-13 08:53:29 2026-03-29 04:53:04 0000-00-00 00:00:00 2026 0 0 34 str. no. Vol. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.89:81'322 0360-0300 10.1145/3787584 268167171 RAZ_Tran_Hanh_Thi_Hong_2026.pdf RAZ_Tran_Hanh_Thi_Hong_2026.pdf 1 1D9DEDCD419E0FA249968031AD7E2FFA f56b3a4e1c283595c0811454f15f3505dfd0c238ca8da94b02b18e3d8bf45e1b 4da55e79-29be-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=231454 https://dl.acm.org/doi/10.1145/3787584 1 ea033388-08b0-11f1-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=227912 Fakulteta za računalništvo in informatiko 0 0 0