Studies of children's language use in the wild (e.g., in the context of child-caregiver social interaction) have been slowed by the time-and resource-consuming task of hand annotating utterances for communicative intents/speech acts. Existing studies have typically focused on investigating rather small samples of children, raising the question of how their ndings generalize both to larger and more representative populations and to a richer set of interaction contexts. Here we propose a simple automatic model for speech act labeling in early childhood based on the INCA-A coding scheme (Ninio et al.,). A er validating the model against ground truth labels, we automatically annotated the entire English-language data from the CHILDES corpus. The major theoretical result was that earlier ndings generalize quite well at a large scale. Further, we introduced two complementary measures for the age of acquisition of speech acts which allows us to rank di erent speech acts according to their order of emergence in production and comprehension. Our model is shared with the community so that researchers can use it with their data to investigate various question related to language use both in typical and atypical populations of children.