  The ability to model and automatically detect dialogue act is an important
step toward understanding spontaneous speech and Instant Messages. However, it
has been difficult to infer a dialogue act from a surface utterance because it
highly depends on the context of the utterance and speaker linguistic
knowledge; especially in Arabic dialects. This paper proposes a statistical
dialogue analysis model to recognize utterance's dialogue acts using a
multi-classes hierarchical structure. The model can automatically acquire
probabilistic discourse knowledge from a dialogue corpus were collected and
annotated manually from multi-genre Egyptian call-centers. Extensive
experiments were conducted using Support Vector Machines classifier to evaluate
the system performance. The results attained in the term of average F-measure
scores of 0.912; showed that the proposed approach has moderately improved
F-measure by approximately 20%.
