Yet since we now have access to significantly more data, one has to wonder what conclusions that have been drawn on small data sets may carry over when these learning methods are trained using much larger corpora. In this paper, we present a study of the effects of data size on machine learning for natural language disambiguation. In particular, we study the problem of selection among confusable words, using orders of magnitude more training data than has ever been applied to this problem. First we show learning curves for four different machine learning algorithms. .
