Suffix sequences based morphological segmentation for Afaan Oromo

This paper reports on a morphological segmentation model for Afaan Oromo based on suffix sequences approach. Understanding and identifying the suffix sequences of a language allow us to detect morpheme boundaries of many words of Afaan Oromo. Morphological segmentation models can be used in many Natural Language Processing applications such as machine translation, speech recognition, information retrieval and part-of-speech tagging. A divisive hierarchical clustering and frequency distribution were used to build a tree of candidate stems from which segmented suffix sequences can be modeled. The proposed morphological segmentation model was evaluated with test word-lists. The accuracy obtained by our morphological segmentation model is encouraging.