방법 비교

	GloVe 임베딩	TF-IDF
분야	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline
기원 연도	2014	1988
창시자	Pennington, Socher & Manning	Salton & Buckley
유형	Static word-embedding model	Text vectorization / term-weighting scheme
원전	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
별칭	GloVe, global vectors, GloVe Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
관련	3	3
요약	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.