We use data from a virtual world game for automated learning of words and grammatical constructions and their meanings. The language data are an integral part of the social interaction in the game and consist of chat dialogue, which is only constrained by the cultural context, as set by the nature of the provided virtual environment. Building on previous work, where we extracted a vocabulary for concrete objects in the game by making use of the non-linguistic context, we now target NP/DP grammar, in particular determiners. We assume that we have captured the meanings of a set of determiners if we can predict which determiner will be used in a particular context. To this end we train a classifier that predicts the choice of a determiner on the basis of features from the linguistic and non-linguistic context.Netherlands Organization for Scientific Research (Rubicon grant, project nr. 446-09-011)