This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hun- dred words, we show that their influence is nonstationary on a much smaller time scale. Empirical data drawn from English and Japanese text, as well as conversational speech,' reveals that the "attraction " between words decays exponentially, while stylistic and syntactic contraints create a "repulsion" between words that discourages close co-occurrence. 'We show that these characteristics are well described by simple mixture models based on twostage exponential distributions which can be trained using the EM algorithm. The resulting distance distributions can then be incorporated as penalizing features in an exponential language model.