Abstract
Long-range correlation, a property of time series exhibiting relevant statistical dependence between two distant subsequences, is mainly studied in the statistical physics domain and has been reported to exist in natural language. By using a state-of-the-art method for such analysis, long-range correlation is first shown to occur in long CHILDES data sets. To understand why, generative stochastic models of language, originally proposed in the cognitive scientific domain, are investigated. Among representative models, the Simon model is found to exhibit surprisingly good long-range correlation, but not the Pitman-Yor model. Because the Simon model is known not to correctly reflect the vocabulary growth of natural languages, a simple new model is devised as a conjunct of the Simon and Pitman-Yor models, such that long-range correlation holds with a correct vocabulary growth rate. The investigation overall suggests that uniform sampling is one cause of long-range correlation and could thus have some relation with actual linguistic processes.