  Community question answering, a recent evolution of question answering in the
Web context, allows a user to quickly consult the opinion of a number of people
on a particular topic, thus taking advantage of the wisdom of the crowd. Here
we try to help the user by deciding automatically which answers are good and
which are bad for a given question. In particular, we focus on exploiting the
output structure at the thread level in order to make more consistent global
decisions. More specifically, we exploit the relations between pairs of
comments at any distance in the thread, which we incorporate in a graph-cut and
in an ILP frameworks. We evaluated our approach on the benchmark dataset of
SemEval-2015 Task 3. Results improved over the state of the art, confirming the
importance of using thread level information.
