A neural probabilistic language model

This paper introduces a neural network-based language model that learns the probability distribution of words in a sequence. It avoids the curse of dimensionality by learning a distributed representation for each word, allowing it to generalize to unseen word sequences. The model demonstrates improved performance in predicting the next word in a sentence compared to traditional n-gram models.