  In natural-language discourse, related events tend to appear near each other
to describe a larger scenario. Such structures can be formalized by the notion
of a frame (a.k.a. template), which comprises a set of related events and
prototypical participants and event transitions. Identifying frames is a
prerequisite for information extraction and natural language generation, and is
usually done manually. Methods for inducing frames have been proposed recently,
but they typically use ad hoc procedures and are difficult to diagnose or
extend. In this paper, we propose the first probabilistic approach to frame
induction, which incorporates frames, events, participants as latent topics and
learns those frame and event transitions that best explain the text. The number
of frames is inferred by a novel application of a split-merge method from
syntactic parsing. In end-to-end evaluations from text to induced frames and
extracted facts, our method produced state-of-the-art results while
substantially reducing engineering effort.
