| Sumario: | Sentence boundary detection (SBD) is an essential step for a very large number of natural language processing applications such as parsing, information retrieval, automatic summarization, machine translation, etc. In this paper, we tackle the problem of SBD of dialectal Arabic, especially for the Tunisian dialect. We compare the efficiency of three learning algorithms: Deep Neuronal Networks (DNN), Support Vector Machines (SVM) and Conditional Random Fields (CRF) to detect the boundaries of sentences written in different types of dialect. The best model achieved an F-measure of 84.37% using CRF which is a popular formalism for structured prediction in NLP and it has been widely applied in text segmentation.
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