방법 비교

	BERT 임베딩	Word2Vec
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	2019	2013
창시자	Devlin, Chang, Lee & Toutanova (Google AI)	Tomas Mikolov et al.
유형	Contextual transformer text-representation method	Neural word-embedding model
원전	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
별칭	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
관련	4	4
요약	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.