AdaptText: A Novel Framework for Domain-Independent Automated Sinhala Text Classification

Abstract

Sinhala language is being the widely used language in Sri Lanka. With the advancement of internet usage in Sri Lanka, an incredible amount of Sinhala text data is being added to the internet. In order to manage, analyze and make decisions from the available text data, it requires text classification. Being a low resource and morphologically rich language requires higher expertise and a considerable amount of budget and time to develop an effective task-specific text classifier. This research aims to develop a domain or dataset agnostic and automated solution to improve the quality and address current research gaps of text classification in Sinhala. Based on the solution, a high-level development framework and a user interface are developed. In addition, we perform a cross-domain evaluation with multiple datasets to evaluate the effectiveness of the solution. The proposed framework achieved state-of-the-art results for the Sinhala text classification.