Сравнение методов

	Встраивания GloVe	TF-IDF
Область	Интеллектуальный анализ текста	Интеллектуальный анализ текста
Семейство	Process / pipeline	Process / pipeline
Год появления	2014	1988
Автор метода	Pennington, Socher & Manning	Salton & Buckley
Тип	Static word-embedding model	Text vectorization / term-weighting scheme
Основополагающий источник	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.
Другие названия	GloVe, global vectors, GloVe Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Связанные	3	3
Сводка	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.