Сравнение на методи

	Word2Vec	GloVe Embeddings	TF-IDF
Област	Извличане на текст	Извличане на текст	Извличане на текст
Семейство	Process / pipeline	Process / pipeline	Process / pipeline
Година на възникване	2013	2014	1988
Създател	Tomas Mikolov et al.	Pennington, Socher & Manning	Salton & Buckley
Тип	Neural word-embedding model	Static word-embedding model	Text vectorization / term-weighting scheme
Основополагащ източник	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.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Други названия	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri	GloVe, global vectors, GloVe Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Свързани	4	3	3
Резюме	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.	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.