Módszerek összehasonlítása

	GloVe beágyazások	Kollokációelemzés	TF-IDF
Tudományterület	Szövegbányászat	Szövegbányászat	Szövegbányászat
Módszercsalád	Process / pipeline	Process / pipeline	Process / pipeline
Keletkezés éve	2014	1990	1988
Megalkotó	Pennington, Socher & Manning	Church & Hanks	Salton & Buckley
Típus	Static word-embedding model	Statistical text-mining technique	Text vectorization / term-weighting scheme
Alapmű	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	Church, K.W. & Hanks, P. (1990). Word Association Norms, Mutual Information, and Lexicography. Computational Linguistics, 16(1), 22-29.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Alternatív nevek	GloVe, global vectors, GloVe Kelime Gömülmeleri	word association, collocation extraction, Birliktelik Analizi (Collocation Analysis)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Kapcsolódó	3	3	3
Összefoglaló	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.	Collocation analysis is a statistical text-mining technique that identifies word pairs or expressions that frequently occur together, using association measures rather than chance co-occurrence. Introduced in the lexicography work of Church and Hanks (1990), it is used for terminology extraction and language analysis, surfacing the multi-word units that carry meaning in a corpus.	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.