Design and Development of Enhanced Morphological Analyzer for Ge'ez Verbs Using Memory Based Learning Algorithms

Abstract

This paper is carefully designed for Ge'ez morphological analyzer. Ge'ez is the classical language of Ethiopia and still used as the liturgical language of Ethiopian Orthodox Tewahedo church. Many ancient literatures were written in Ge'ez. The literature includes religious texts and secular writings. The ancient philosophy, tradition, history and knowledge of Ethiopia were being written in Ge'ez. Morphological analyzer is one of the most important basic tools in automatic processing of any human language and analyses the naturally occurring word forms in a sentence and identifies the root word and its features.  

In this paper, MBL is used to automatically analyze the morphology of Ge'ez verbs via the concept of machine learning for training and analysis. TiMB's IB2 and TRIBL2 algorithms have been used for the implementation. The performance of the system has been evaluated using 10-fold cross validation technique on the default and optimized parameter settings. The overall accuracy with optimized parameters using IB2 and TRIBL2 was 94.24% and 93.31%, respectively. Similarly, the overall precision, recall and F-score with optimized parameters using IB2 were 55.6%, 56.3% and 59.95%, respectively. In the same manner the precision, recall and F-score using TRIBL2 were 58.8%, 60.3% and 59.54%, respectively. Moreover, a learning curve was drawn.  The graph showed that as the number of training dataset increase, the accuracy on unseen data can be increased. Therefore, IB2 algorithm shows better result thanTRIBL2 algorithm for Ge'ez verb morphology.