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The article introduces corrections to Zipf's and Heaps' laws based onsystematic models of the hapax rate. The derivation rests on two assumptions:The first one is the standard urn model which predicts that marginal frequencydistributions for shorter texts look as if word tokens were sampled blindlyfrom a given longer text. The second assumption posits that the rate of hapaxesis a simple function of the text size. Four such functions are discussed: theconstant model, the Davis model, the linear model, and the logistic model. Itis shown that the logistic model yields the best fit.

Corrections of Zipf's and Heaps' Laws Derived from Hapax Rate Models