Abstract
Although natural language processing (NLP) is now a popular area of research and development, less-resourced languages are not receiving much attention from developers. One of such under-resourced languages is Kafi-noonoo which is spoken in the south-western regions of Ethiopia. This paper presents the development of part-of-speech tagger for Kafi-noonoo. In order to develop the tagger, we employed a hybrid of two systems: statistical and rule-based taggers. The lexical and transitional probabilities of word classes are modeled using HMM. However, due to the limitation of corpus for the language, a set of transformation rules are applied to improve the result. The system was tested with test corpus and, with 90% of the corpus used for training, the hybrid tagger yielded an accuracy of 80.47%.
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Mekuria, Z., Assabie, Y. (2014). A Hybrid Approach to the Development of Part-of-Speech Tagger for Kafi-noonoo Text . In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54906-9_17
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DOI: https://doi.org/10.1007/978-3-642-54906-9_17
Publisher Name: Springer, Berlin, Heidelberg
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