Journal of the American Society for Information Science and
Technology, 2009, in press.
This paper explores computational models of multi-party discourse, using transcripts
from U.S. Supreme Court oral arguments. The turn-taking behavior of participants is
treated as a supervised sequence labeling problem and modeled using first- and secondorder
Conditional Random Fields. We specifically explore the hypothesis that discourse
markers and personal references provide important features in such models. Results
from a sequence prediction experiment demonstrate that incorporating these two types
of features yields significant improvements in performance. This work is couched in the
broader context of developing tools to support legal scholarship, although we see other
NLP applications as well.