  We develop a probabilistic latent-variable model to discover semantic
frames---types of events and their participants---from corpora. We present a
Dirichlet-multinomial model in which frames are latent categories that explain
the linking of verb-subject-object triples, given document-level sparsity. We
analyze what the model learns, and compare it to FrameNet, noting it learns
some novel and interesting frames. This document also contains a discussion of
inference issues, including concentration parameter learning; and a small-scale
error analysis of syntactic parsing accuracy.
