A Neural Probabilistic Language Model

This paper introduces a neural network-based language model that learns distributed representations for words, enabling it to predict the probability of word sequences. This model overcomes the curse of dimensionality by learning a distributed representation of words, allowing for better generalization and prediction of unseen word combinations, which is crucial for applications like email marketing personalization and segmentation as employed by tools like GetResponse.