Vertaile menetelmiä

	Itseohjautuva Word2Vec	GloVe-upotukset	Rekurrentti neuroverkko
Tieteenala	Syväoppiminen	Tekstinlouhinta	Syväoppiminen
Menetelmäperhe	Machine learning	Process / pipeline	Machine learning
Syntyvuosi	2013	2014	1986–1990
Kehittäjä	Mikolov, T., Chen, K., Corrado, G., & Dean, J.	Pennington, Socher & Manning	Rumelhart, D. E.; Elman, J. L.
Tyyppi	Self-supervised neural word embedding	Static word-embedding model	Sequential neural network
Alkuperäislähde	Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR 2013).	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Rinnakkaisnimet	Word2Vec, word embeddings, Skip-gram model, CBOW model	GloVe, global vectors, GloVe Kelime Gömülmeleri	RNN, Elman network, Jordan network, simple recurrent network
Liittyvät	3	3	3
Tiivistelmä	Word2Vec is a shallow neural network model introduced by Mikolov et al. (2013) that learns dense vector representations of words from large unlabeled text corpora using self-supervised objectives. By training a model to predict surrounding context words (Skip-gram) or a target word from its context (CBOW), it captures rich semantic and syntactic regularities in continuous vector space without any manual annotation.	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.