Abstract
The main aim of this study is to develop part-of-speech tagger for Afaan Oromo language. After reviewing literatures on Afaan Oromo grammars and identifying tagset and word categories, the study adopted Hidden Markov Model (HMM) approach and has implemented unigram and bigram models of Viterbi algorithm. Unigram model is used to understand word ambiguity in the language, while bigram model is used to undertake contextual analysis of words. For training and testing purpose 159 sentences (with a total of 1621 words) that are manually annotated sample corpus are used. The corpus is collected from different public Afaan Oromo newspapers and bulletins to make the sample corpus balanced. A database of lexical probabilities and transitional probabilities are developed from the annotated corpus. These two probabilities are from which the tagger learn and tag sequence of words in sentences. The performance of the prototype, Afaan Oromo tagger is tested using tenfold cross validation mechanism. The result shows that in both unigram and bigram models 87.58% and 91.97% accuracy is obtained, respectively.
Highlights
At the heart of any natural language processing (NLP) task, there is the issue of natural language understanding
As explained in [1], natural languages give rise to lexical ambiguity that words may have different meanings, i.e. one word is in general connected with different readings in the lexicon
On Amharic language, two researches were conducted on POS tagging by [5] and [11], but to the best of our knowledge there is no POS tagging research conducted for Afaan Oromo language
Summary
At the heart of any natural language processing (NLP) task, there is the issue of natural language understanding. In the above particular context suffixes are added to show gender {–t, --ta}, number { –tu/--u} and future {--fi} To handle such complexities and use computers to understand and manipulate natural language text and speech, there are various research attempts under investigation. Some of these include machine translation, information extraction and retrieval using natural language, text to speech synthesis, automatic written text recognition, grammar checking, and part-of-speech tagging. Most of these approaches have been developed for popular languages like English [3]. The study presents the investigation of designing and developing an automatic part-of-speech tagger for Afaan Oromo language
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