Salīdzināt metodes

	Word2Vec	Dokumentu kopu grupēšana	GloVe iegulšanas	TF-IDF
Nozare	Teksta ieguve	Teksta ieguve	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	2013	—	2014	1988
Autors	Tomas Mikolov et al.	—	Pennington, Socher & Manning	Salton & Buckley
Tips	Neural word-embedding model	Unsupervised text-mining task	Static word-embedding model	Text vectorization / term-weighting scheme
Pirmavots	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	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.
Citi nosaukumi	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	GloVe, global vectors, GloVe Kelime Gömülmeleri	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Saistītās	4	4	3	3
Kopsavilkums	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.	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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.