In this paper, we propose a new method for semantic class induction. First, we introduce a generative model of sentences, based on dependency trees and which takes into account homonymy. Our model can thus be seen as a generalization of Brown clustering. Second, we describe an efficient algorithm to perform inference and learning in this model. Third, we apply our proposed method on two large datasets ($10^8$ tokens, $10^5$ words types), and demonstrate that classes induced by our algorithm improve performance over Brown clustering on the task of semi-supervised supersense tagging and named entity recognition.