This paper proposes a lexical acquisition framework for a closed-domain chatbot. It learns the ontological categories of unknown terms in dialogues through implicit confirmation instead of using explicit questions that disrupt the flow of conversation. Our system generates an implicit confirmation request containing an unknown term’s category prediction, which may be incorrect. It then acquires the category only if its prediction was correct by checking various cues that appeared during the confirmation process. We divide this process into two steps. First, we propose a two-tiered method to predict unknown term categories that attempts to predict the most specific category and backs off to a more general category when it is insufficiently confident about its prediction. Direct evaluation showed that this two-tiered method makes correct category predictions 54.4% more often than that predicting the most specific category only. Next, we propose a method for identifying whether categories included confirmation requests are correct by using both the user response following the confirmation request and its context. We introduce features, which are derived from analysis of the confirmation process, and construct a classifier from chat data, which we collect with crowdsourcing. We show that the classifier can identify correct ategories with a precision of 0.708.