  Argument mining systems often consider contextual information, i.e.
information outside of an argumentative discourse unit, when trained to
accomplish tasks such as argument component identification, classification, and
relation extraction. However, prior work has not carefully analyzed the utility
of different contextual properties in context-aware models. In this work, we
show how two different types of contextual information, local discourse context
and speaker context, can be incorporated into a computational model for
classifying argument components in multi-party classroom discussions. We find
that both context types can improve performance, although the improvements are
dependent on context size and position.
