A Hierarchical Bayesian Model for Topic Segmentation
Many streams of real-world data, such as conversations or bo dy movements, consist of relatively coherent segments, each chara terized by particular topics or controllers. Making sense of these data re qui s simultaneously segmenting the sequences and inferring the struc ture of the segments. We present a hierarchical Bayesian model that can be used to break a sequence of utterances or movements into segments with different distributions over topics or controllers. We apply t his model to a database of meetings, showing that its unsupervised segme ntation is competitive with other approaches, and a database of human h and movements, revealing some of the controllers for motions of the h and.