Confronta i metodi

	Embedding di parole GloVe	Analisi delle Collocazioni	TF-IDF
Campo	Text mining	Text mining	Text mining
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	2014	1990	1988
Ideatore	Pennington, Socher & Manning	Church & Hanks	Salton & Buckley
Tipo	Static word-embedding model	Statistical text-mining technique	Text vectorization / term-weighting scheme
Fonte seminale	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.
Alias	GloVe, global vectors, GloVe Kelime Gömülmeleri	word association, collocation extraction, Birliktelik Analizi (Collocation Analysis)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Correlati	3	3	3
Sintesi	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.