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

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 t

SUBMITTER: Shikali CS 

PROVIDER: S-EPMC7339006 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature