  Predicting the structure of a discourse is challenging because relations
between discourse segments are often implicit and thus hard to distinguish
computationally. I extend previous work to classify implicit discourse
relations by introducing a novel set of features on the level of semantic
roles. My results demonstrate that such features are helpful, yielding results
competitive with other feature-rich approaches on the PDTB. My main
contribution is an analysis of improvements that can be traced back to
role-based features, providing insights into why and when role semantics is
helpful.
