Enhancing African low-resource languages: Swahili data for language modelling

Data Brief. 2020 Jun 30;31:105951. doi: 10.1016/j.dib.2020.105951. eCollection 2020 Aug.

Abstract

Language modelling using neural networks requires adequate data to guarantee quality word representation which is important for natural language processing (NLP) tasks. However, African languages, Swahili in particular, have been disadvantaged and most of them are classified as low resource languages because of inadequate data for NLP. In this article, we derive and contribute unannotated Swahili dataset, Swahili syllabic alphabet and Swahili word analogy dataset to address the need for language processing resources especially for low resource languages. Therefore, we derive the unannotated Swahili dataset by pre-processing raw Swahili data using a Python script, formulate the syllabic alphabet and develop the Swahili word analogy dataset based on an existing English dataset. We envisage that the datasets will not only support language models but also other NLP downstream tasks such as part-of-speech tagging, machine translation and sentiment analysis.

Keywords: Deep learning; Language modelling; Natural language processing; Neural networks; Syllables; Unannotated data; Word analogy.