A Hybrid Approach to the Development of Part-of-Speech Tagger for Kafi-noonoo Text

摘要

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%.