Sammenlign metoder

	Doc2Vec	GloVe-innleiringer	TF-IDF
Fagfelt	Tekstutvinning	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Opprinnelsesår	2014	2014	1988
Opphavsperson	Quoc V. Le & Tomas Mikolov	Pennington, Socher & Manning	Salton & Buckley
Type	Document-embedding representation learning	Static word-embedding model	Text vectorization / term-weighting scheme
Opprinnelig kilde	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	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.
Alias	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	GloVe, global vectors, GloVe Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relaterte	4	3	3
Sammendrag	Doc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectors place similar documents close together in space, supporting document comparison and classification.	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.