= Yoshua Bengio
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<Yoshua Bengio> coauthored a 2003 <neural language model> that learns <word embeddings> jointly with a probability model for word sequences. The aim is to share statistical information among related contexts rather than assign unrelated parameters to every observed sequence.<A Neural Probabilistic Language Model by Yoshua Bengio and others>[abstract and model description]{ref}
For example, a learned vector for a word can contribute to predictions in many contexts. Updating that vector through one <training data>[training example] can therefore affect predictions elsewhere, providing a mechanism for <generalization>. This is an explicit computational use of a <distributed representation>, not a claim that nearby vectors always encode the same linguistic relationship. Bengio also coauthored <Deep Learning by Ian Goodfellow and others>.
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