| 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. |
