GE’EZ SYNTAX ERROR DETECTION USING DEEP LEARNING

HABTAMU, SHIFERAW ASMARE

dc.contributor.author	HABTAMU, SHIFERAW ASMARE
dc.date.accessioned	2024-12-06T07:49:20Z
dc.date.available	2024-12-06T07:49:20Z
dc.date.issued	2023-07
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/16305
dc.description.abstract	The Ge'ez language, an ancient Ethiopian Semitic language still used in liturgical contexts and taught at university and college levels, lacks tools for part-of-speech tagging, morphological analyzers, and syntax error detection in written texts. This hinders the identification of syntax errors and poses a significant challenge for learners, and researchers. This study addresses this problem by developing a morphological part of speech tagging and syntax error detection models for Ge'ez using deep learning approaches. To develop the model, a dataset of 4,981 sentences that have 30326 words and 11,747 unique words was collected for part-of-speech tagging. Additionally, a dataset of 1,170 sentences was collected for syntax error detection. LSTM and BiLSTM algorithms were used to develop the models. The LSTM model achieved an accuracy of 94% and 92.31% in the Gz-POS and Gz-SED tasks, respectively, and the BiLSTM model achieved an accuracy of 95.01% and 94.02% in the Gz-POS and Gz-SED. The BiLSTM model outperformed the LSTM model with some accuracy differences. The results demonstrate the effectiveness of deep learning algorithms for part-of-speech tagging and syntax error detection in the Ge'ez language. The developed models provide a feasible solution to the challenges of digitizing Ge'ez books, and a help for second-language learners. The findings contribute to the improvement of language education, research, and development in under-resourced languages. Future researchers can use the developed models and methodology as a framework for further advancements in Ge'ez language processing. Keywords: - Ge’ez Morphological POS Tagging, Ge’ez Syntax Error Detection, Deep Learning, LSTM, BiLSTM.	en_US
dc.language.iso	en_US	en_US
dc.subject	Information Technology	en_US
dc.title	GE’EZ SYNTAX ERROR DETECTION USING DEEP LEARNING	en_US
dc.type	Thesis	en_US