A Systematic Literature Review of Hausa Natural Language Processing

The processing of natural languages is an area of computer science that has gained growing attention recently. NLP helps computers recognize, in other words, the ways in which people use their language. NLP research, however, has been performed predominantly on languages with abundant quantities of annotated data, such as English, French, German and Arabic. While the Hausa Language is Africa's second most commonly used language, only a few studies have so far focused on Hausa Natural Language Processing (HNLP). In this research paper, using a keyword index and article title search, we present a systematic analysis of the current literature applicable to HNLP in the Google Scholar database from 2015 to June 2020. A very few research papers on HNLP research, especially in areas such as part-of-speech tagging (POS), Name Entity Recognition (NER), Words Embedding, Speech Recognition and Machine Translation, have just recently been released. This is due to the fact that for training intelligent models, NLP depends on a huge amount of human-annotated data. HNLP is now attracting researchers' attention after extensive research on NLP in English and other languages has been performed. The key objectives of this paper are to promote research, to define likely areas for future studies in the HNLP, and to assist in the creation of further examinations by researchers for relevant studies.