  Text segmentation task is an essential processing task for many of Natural
Language Processing (NLP) such as text summarization, text translation,
dialogue language understanding, among others. Turns segmentation considered
the key player in dialogue understanding task for building automatic
Human-Computer systems. In this paper, we introduce a novel approach to turn
segmentation into utterances for Egyptian spontaneous dialogues and Instance
Messages (IM) using Machine Learning (ML) approach as a part of automatic
understanding Egyptian spontaneous dialogues and IM task. Due to the lack of
Egyptian dialect dialogue corpus the system evaluated by our corpus includes
3001 turns, which are collected, segmented, and annotated manually from
Egyptian call-centers. The system achieves F1 scores of 90.74% and accuracy of
95.98%.
