  This article presents an analysis of the influence of context information on
dialog act recognition. We performed experiments on the widely explored
Switchboard corpus, as well as on data annotated according to the recent ISO
24617-2 standard. The latter was obtained from the Tilburg DialogBank and
through the mapping of the annotations of a subset of the Let's Go corpus. We
used a classification approach based on SVMs, which had proved successful in
previous work and allowed us to limit the amount of context information
provided. This way, we were able to observe the influence patterns as the
amount of context information increased. Our base features consisted of
n-grams, punctuation, and wh-words. Context information was obtained from one
to five preceding segments and provided either as n-grams or dialog act
classifications, with the latter typically leading to better results and more
stable influence patterns. In addition to the conclusions about the importance
and influence of context information, our experiments on the Switchboard corpus
also led to results that advanced the state-of-the-art on the dialog act
recognition task on that corpus. Furthermore, the results obtained on data
annotated according to the ISO 24617-2 standard define a baseline for future
work and contribute for the standardization of experiments in the area.
