Volume 11, Number 3

Prototyping NLP Non-Word Detection System for Dinka using Dictionary Lookup Approach

  Authors

Manyok Chol David¹ and Robert Balagadde Ssali², ¹University of Juba, South Sudan, ²Kampala International University, Uganda

  Abstract

Spellcheckers are computer software used for non-word or real word error detection. The Dinka text editors have been developed, however, no one has developed their spellcheckers. The research entitled Prototyping NLP Non-Word Detection System for Dinka Using Dictionary Lookup Approach was a solution to Dinka spellchecking. The study objectives were: requirements gathering and analysis. The computer keyboard was customized to accept the Dinka characters. Dinka lexicon was created with 6,976 words. The prototype was implemented using java programming language and dictionary lookup approach was used for non-word detection. The accuracy of detection (detecting real words and non-words) gave 98.10%, and the accuracy of non-word detection (detection of non-words only) was 91.36%. The True Positive Rate (TPR) was 99.10% and the True Negative Rate (TNR) was 91.36 %. The speed of non-word detection which was found as1, 044 Hz was slow.

  Keywords

Dinka, lexicon, non-word, detection, correction, prototype.