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A Neural Probabilistic Language Model

Journal of Machine Learning Research 3 (2003) 1137 1155 Submitted 4/02; Published 2/03A Neural Probabilistic Language ModelYoshua jean partement d Informatique et Recherche Op rationnelleCentre de Recherche Math matiquesUniversit de Montr al, Montr al, Qu bec, CanadaEditors:Jaz Kandola, Thomas Hofmann, Tomaso Poggio and John Shawe-TaylorAbstractA goal of statistical Language modeling is to learn the joint probability function of sequences ofwords in a Language . This is intrinsically difficult because of thecurse of dimensionality:awordsequence on which the Model will be tested is likely to be different from all the word sequences seenduring training. Traditional but very successful approaches based on n-grams obtain generalizationby concatenating very short overlapping sequences seen in the training set. We propose to fight thecurse of dimensionality bylearning a distributed representation for wordswhich allows eachtraining sentence to inform the Model about an exponential number of semantically neighboringsentences.

Journal of Machine Learning Research 3 (2003) 1137–1155 Submitted 4/02; Published 2/03 A Neural Probabilistic Language Model Yoshua Bengio BENGIOY@IRO.UMONTREAL.

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