So sánh phương pháp

	Word2Vec	TF-IDF
Lĩnh vực	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2013	1988
Người khởi xướng	Tomas Mikolov et al.	Salton & Buckley
Loại	Neural word-embedding model	Text vectorization / term-weighting scheme
Công trình gốc	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Tên gọi khác	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Liên quan	4	3
Tóm tắt	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.	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.