I'm reading "Recurrent neural network based language model" of Mikolov et al. (2010). Although the article is straight forward, I'm not sure how word embedding $w(t)$ is obtained:
The reason I wonder is that in the classic "A Neural Probabilistic Language Model" Bengio et al. (2003) - they used separate embedding vector for representing each word and it was somehow "semi-layer", meaning - it haven't contains non-linearity, but they did update word embeddings during the back-propagation.
In Mikolov approach though, I assume they used simple one-hot vector, where each feature represent presence of each word. If we represent that's way single word input (like was in the Mikolov's paper) - that vector become all-zeros except single one.
Is that correct?