DEVELOPMENT OF A GEEZ GRAMMAR CHECKER USING A HYBRID APPROACH

Abstract

Grammar checking is a fundamental task in Natural Language Processing (NLP), aimed at verifying the grammatical correctness of a given text. In the contemporary digital era, humans employ various platforms like websites and mobile apps to produce copious amounts of text. For the purpose of suitable communication, suitable rules of grammar should be used. but while proofreading huge chunks of texts manually, it becomes tedious, prone to errors, and not possible. Thus, there have been programs of automated grammar checkers for many languages like English, Amharic, and Afan Oromo. Despite its historical and liturgical importance, the Geez language currently lacks a dedicated grammar checking system. This thesis addresses this gap by proposing a grammar checker specifically designed for Geez. The proposed system employs a hybrid approach that combines rule-based methods with statistical techniques to identify grammatical errors related to subject-verb agreement, object-verb agreement, adverb-verb agreement, and word order. The system has five main modules namely Preprocessing, Bigram Formation, Rule based grammar checker, Statistical grammar checker, and Display Grammar Checking Result to process the input text and to build the bigram of the system. It is developed using Python programming language utilizing different IDEs such as Jupyter Notebook and PyCharm IDEs, and it employs a MySQL database for handling linguistic data. The database of the system consists of more than 18,000 Geez sentences as corpus. The rule-based part of the system checks Subject-Verb, Object- Verb, and Adverb-Verb agreement errors using 360 handwritten correct grammar rules. The statistical module detects WSA errors based on unique word sequences and a probabilistic language model. The system was evaluated through three separate experiments using a dataset of 400 manually prepared sentences, containing both grammatically correct and incorrect examples. The results demonstrate that the system achieved strong overall performance, with an average precision of 88.09%, recall of 89.35%, and F-measure of 88.68%. These results demonstrate how will and consistently the suggested grammar checker can detect grammatical mistakes in Geʽez text.