SENTENCE-LEVEL GRAMMAR CHECKER FOR KAMBAATISSA LANGUAGE USING DEEP LEARNING APPROACH

Abstract

In the modern world, the most basic and culturally accepted means of communication is language, and the use of grammar is crucial to language fluency. Finding grammatical errors in natural language processing applications involves checking whether the words in a sentence conform to the predefined grammar rules for number, gender, tense, and the necessary information to convey the information in written language. Incorrect input sentences can have agreement problems, such as subject-verb, adjective-noun, and adverb verb agreement problems. This study developed a sentence-level grammar checker for the Kambaatissa language using a deep learning approach. In particular, we focus on implementations of gating methods such as the LSTM class and the more recently proposed GRU. For the development of the proposed model, Python programming languages and packages were used. Among the packages, the TensorFlow and Keras packages can effectively perform grammar error checking of the proposed model. GRU and LSTM test cases were used for evaluation. Finally, the test results show that the LSTM accuracy is 83% recall, 83% precision, 83%, f1_score is 83% and kappa score is 78%. The GRU performed 83% accuracy, 83% of recall, 83% precision and 83% f1_score and kappa score is 77%. The main challenge of this study was the rich and complex morphology of the Kambaatissa language and to find a sufficient amount of Kambaatissa sentence. The results of this study help to advance the Kambaatissa language's grammar checking technology. For writers, students, and language learners seeking to ensure that written text is grammatically correct and consistent, advanced grammar proofreading is an invaluable resource. Future research directions include expanding the coverage of the grammar checker to handle more complex grammatical constructions and integrating it with textual support models for wider usabilitY