TOWARDS EFFICIENT PART-OF-SPEECH TAGGING FOR THE KANURI LANGUAGE: A HIDDEN MARKOV MODEL-BASED SOLUTION
Kanuri is a Nilo-Saharan language spoken in the Lake Chad basin of West and Central Africa. Effective part-of-speech (POS) tagging is crucial for natural language processing tasks in Kanuri, such as machine translation, information extraction, and text generation. However, the lack of comprehensive linguistic resources and annotated datasets for Kanuri has hindered the development of accurate POS taggers for this language. This study aims to develop a part-of-speech tagger for the Kanuri language using a Hidden Markov Model (HMM) approach. The goal is to create a robust and accurate POS tagging system that can be used to support various natural language processing applications for the Kanuri language. The study involved a development of corpus of Kanuri text collected from various sources. A HMM-based POS tagging model was designed and trained on the annotated Kanuri corpus. The HMM-based POS tagger achieved an overall accuracy of accuracy of 0.827 % on the Kanuri test data. The developed HMM-based POS tagger can be integrated into various natural language processing pipelines for Kanuri, enabling more advanced language analysis and understanding tasks. Additionally, the annotated Kanuri corpus can be used to expand and improve the POS tagging model further and support the development of other language technologies for the Kanuri language.
Authors : Tukur, A., Jibrin, A. and Inuwa, U.
Category : Open Access Volume (Issue) : 10(2) Date Uploaded : 29th October 2024