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The ability to model and automatically detect dialogue act is an importantstep toward understanding spontaneous speech and Instant Messages. However, ithas been difficult to infer a dialogue act from a surface utterance because ithighly depends on the context of the utterance and speaker linguisticknowledge; especially in Arabic dialects. This paper proposes a statisticaldialogue analysis model to recognize utterance's dialogue acts using amulti-classes hierarchical structure. The model can automatically acquireprobabilistic discourse knowledge from a dialogue corpus were collected andannotated manually from multi-genre Egyptian call-centers. Extensiveexperiments were conducted using Support Vector Machines classifier to evaluatethe system performance. The results attained in the term of average F-measurescores of 0.912; showed that the proposed approach has moderately improvedF-measure by approximately 20%.

Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level