word n-gram language model
Probabilistic language model that predicts the next word from the previous N words using observed text frequencies, without any learned representations.
1 milestone
A Neural Probabilistic Language Model by Yoshua Bengio and Colleagues
In 2003, Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin at the Université de Montréal published 'A Neural Probabilistic Language Model' in JMLR, demonstrating that a feed-forward neural network trained on word sequences could learn distributed word representations and outperform n-gram models on perplexity benchmarks.