The results clearly show that the thread-level features are important, providing consistent improvement for all our learning models.
the linear-chain models fail to exploit the sequential dependencies between nearby answer labels to improve the results significantly: although the labels from the neighboring answers can affect the label of the current answer, this dependency is too loose to have impact on the selection accuracy.