A Neural Probabilistic Language Model

2003

Abstract

Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set.

We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences.

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