This paper investigates the role of input frequency in the acquisition of verb argument structures based on distributional information of a corpus of utterances derived from the English CHILDES database (MacWhinney 1993). It has been widely accepted that children successfully learn verb argument structures by innate language mechanisms, such as linking rules which connect verb meanings and its syntactic structures. In contrast, an approach to language acquisition called “statistical language learning” has currently claimed that children could succeed in acquiring syntactic structures in the absence of innate language mechanisms, making use of distributional properties of the input. In this paper, I evaluate the feasibility of the statistical learning in acquiring verb argument structures, based on distributional information about locative verbs in parental input. The naturalistic data allow us to investigate to what extent the statistical learning approach can and cannot help children succeed in learning the syntax of locative verbs. Based on the results of English database analysis, I show that there is rich statistical information for learning the syntactic possibilities of locative verbs in parental input, despite some limitations in the statistical learning approach.