Сравнение на методи

	GloVe Embeddings	Рекурентна невронна мрежа
Област	Извличане на текст	Дълбоко обучение
Семейство	Process / pipeline	Machine learning
Година на възникване	2014	1986–1990
Създател	Pennington, Socher & Manning	Rumelhart, D. E.; Elman, J. L.
Тип	Static word-embedding model	Sequential neural network
Основополагащ източник	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.
Други названия	GloVe, global vectors, GloVe Kelime Gömülmeleri	RNN, Elman network, Jordan network, simple recurrent network
Свързани	3	3
Резюме	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.