방법 비교

	Word2Vec	GloVe 임베딩
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	2013	2014
창시자	Tomas Mikolov et al.	Pennington, Socher & Manning
유형	Neural word-embedding model	Static word-embedding model
원전	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.
별칭	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri	GloVe, global vectors, GloVe Kelime Gömülmeleri
관련	4	3
요약	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.	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.