Context-Based Text Completion System for Amharic Language

Abstract

This paper discusses the practical aspects of easiness in communication using Short Message Service (SMS), E-mailing, correcting misspelt words and checking the grammatical mistakes. There are different data entry mechanisms to insert a text on the computer machine as well as a mobile device, such as a keyboard, soft keys, speech etc. The paper proposed to develop a contextbased auto text completion system for the Amharic language specifically to correct misspelling on Short Message Service (SMS), E-mailing and helps to correct the grammar mistakes as well. Data entry technique can be inserted with the support of text completion (predictive) or non-predictive. Therefore, we are using a statistical model, Predictive Partial Match (PPM) and Support Vector Machine (SVM) approaches for implementing the Amharic contextbased text completion system. Since the system is developed by using the context-based and statistical model, we adopted the Amharic Part of Speech (POS) tagger system. For training and testing the system, we are using 395,464 unique words with frequency and 750,000 sentences that has been prepared by the Walta Information Centre (WIA) and Ethiopia News Agency (ENA). All those data have been used to build the Amharic dictionary, the corpus of the system and to calculate the frequency occurrences of each word as well. Finally, the results show a 14% improvement from traditional frequency-based Amharic word prediction system.