Abstract
POS tagging serves as a preliminary task for many NLP applications. It refers to the process of classifying words into their parts of speech (also known as words classes or lexical categories). Somali is a member of the Cushitic languages with limited number of NLP tools for use. An accurate and reliable POS tagger is essential for many NLP tasks like shallow parsing, dependency parsing, sentiment analysis, and named entity recognition. In this paper, we present a statistical POS tagger for Somali language using different machine learning approaches (i.e., HMM and CRF) and neural network model. Our Somali POS tagger outperforms the state-of-the-art POS tagger by 87.51% on a tenfold cross-validation. The key contribution of this paper are (1) building a generic POS tagger, (2) comparing the performances with the existing state of the art techniques, and (3) exploring the use word embeddings for Somali POS tagging.
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