방법 비교

	도메인 적응형 BERT 기반 분류	문장 임베딩
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2019–2020	2015–2019
창시자	Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
유형	Domain-adaptive pre-training followed by supervised fine-tuning	Representation learning / embedding
원전	Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.	Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.
별칭	DAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPT	sentence vectors, sentence representations, SBERT, semantic sentence encoding
관련	6	4
요약	Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.	Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.