This paper describes work in developing probabilistic classifiers for a discourse segmentation problem that involves segmentation, reference resolution, and belief. Specifically, the problem is to segment a text into blocks such that all subjective sentences in a block are from the point of view of the same agent, and to identify noun phrases that refer to that agent. In our method for developing classifiers, rather than making assumptions about which variables to use and how they are related, statistical techniques are used to explore these questions empiricaily. Further, the types of models used in this work can express complex relationships among diverse sets of variables. This work is part of a large project that is in an early stage of development. The contributions of this paper are an illustration of framing a high-level discourse problem in such a way that it is amenable to statistical processing while still retaining its core, and a description of a method for developing probabilistic classifiers that is well-suited for addressing problems in discourse processing.