Design and Development of Morphological Analyzer for Tigrigna Verbs using Hybrid Approach

Design and Development of Morphological Analyzer for Tigrigna Verbs using Hybrid Approach   Authors Hagos Gebremedhin Gebremeskel1,2, Feng Chong1,* and Huang Heyan1, 1Beijing Institute of Technology, China, 2Mekelle University, Ethiopia   Abstract Morphological analyzer is the basic for various high level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy on the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had the actual performance of 95.6% using linguistic rules for handling irregular and copula verbs.   Keywords Morphological Analyzer; Tigrigna verb Morphology; Machine learning; Character Based Analysis; Feature Extraction; Rules setting; Hybrid approach