WORD SEQUENCE PREDICTION FOR AFAAN OROMO USING CONDITIONAL RANDAM FIELD APPROACH

Major Advisor: Dr.Getachew Mamo Abstract Word prediction is a popular machine learning task, which consists of predicting the next word in sequence of words. Literature shows that word sequence prediction could play a great role in real life applications including electronic based data entry. Word prediction deals with guessing what word comes after, based on some current information, and it is the main focus of this study. Even though Afaan Oromo is used by a large number of populations, few works are done on the topic of word sequence prediction. Previous works on word prediction shows that statistical methods are not enough with highly inflected language and needs syntactical information. In this study, we developed Afaan Oromo word sequence prediction following the Design science research methodology with statistical methods using Conditional Random Field. We used 225,352 words 150,000, phrases to train the model by incorporating detailed parts of speech, for stem/Root and morphological features respectively. The experiments were CRF model on a window size of three, five and seven. We explained the efficacy of Stem/Root Word, morphological feature, and part of speech tag in Afaan Oromo word sequence prediction. Evaluation was performed using developed model and keystroke savings (KSS) as a metrics. According to our test, prediction results using a CRF with detailed Parts of Speech tag model has higher KSS and performed slightly better compared to those without Parts of Speech tag. Therefore, statistical approach with detailed POS with window size of seven has good potential on word sequence prediction for Afaan Oromo language. Keywords: Word sequence prediction, Stem/Root, Parts of Speech, CRF