Detection and Correction of Amharic Grammar Errors Using Deep Learning

Abstract

Effective communication in natural or human language relies heavily on grammatical accuracy. As a result, natural language processing (NLP) has emerged as a critical area of research, aiming to enhance computer’s ability to comprehend and interact using human language. A sentence is considered grammatically correct when its word structure adheres to rules governing number, person, gender, tense, and other grammatical agreements. Numerous studies have explored various languages and grammatical frameworks to develop methods for verifying the grammatical accuracy of sentences. The main aim of this study is to create and execute a system based on deep learning for identifying and rectifying grammatical mistakes in the Amharic language. The suggested method employs a Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN), developed using Python 3.7, with Keras and TensorFlow serving as the backend. The evaluation of the BiLSTM model revealed an accuracy of 88.89%, along with a recall of 88.89%, precision of 89%, and an F1 score of 89%. A significant challenge faced during this research was dealing with the complexity of Amharic words, which can have multiple meanings or reflect different levels of respect, thereby introducing ambiguity into the error detection and correction process. To enhance the effectiveness of detecting and correcting grammatical errors, it is crucial to include a comprehensive dataset of morphologically annotated sentences. Furthermore, future investigations should aim to refine the model and examine alternative methodologies to improve the system's overall performance and accuracy.