  We investigate the origin of Zipf's law for words in written texts by means
of a stochastic dynamical model for text generation. The model incorporates
both features related to the general structure of languages and memory effects
inherent to the production of long coherent messages in the communication
process. It is shown that the multiplicative dynamics of our model leads to
rank-frequency distributions in quantitative agreement with empirical data. Our
results give support to the linguistic relevance of Zipf's law in human
language.
