Most existing topic models make the bagof-words assumption that words are generated independently, and so ignore potentially useful information about word order. Previous attempts to use collocations (short sequences of adjacent words) in topic models have either relied on a pipeline approach, restricted attention to bigrams, or resulted in models whose inference does not scale to large corpora. This paper studies how to simultaneously learn both collocations and their topic assignments. We present an efficient reformulation of the Adaptor Grammar-based topical collocation model (AG-colloc) (Johnson, 2010), and develop a point-wise sampling algorithm for posterior inference in this new formulation. We further improve the efficiency of the sampling algorithm by exploiting sparsity and parallelising inference. Experimental results derived in text classification, information retrieval and human evaluation tasks across a range of datasets show that this reformulation scales to hundreds of thousands of documents while maintaining the good performance of the AG-colloc model.