Salīdzināt metodes

	Doc2Vec	GloVe iegulšanas	Word2Vec
Nozare	Teksta ieguve	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	2014	2014	2013
Autors	Quoc V. Le & Tomas Mikolov	Pennington, Socher & Manning	Tomas Mikolov et al.
Tips	Document-embedding representation learning	Static word-embedding model	Neural word-embedding model
Pirmavots	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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Citi nosaukumi	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	GloVe, global vectors, GloVe Kelime Gömülmeleri	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Saistītās	4	3	4
Kopsavilkums	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.	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.