A Hybrid Framework for Automated News Classification for the Low-Resource Assamese Language

Abstract

Assamese is a low resource language that presents significant challenges for automated news classification due to the scarcity of curated datasets. To address these challenges, this paper introduces a dedicated corpus of 6,582 Assamese news articles and proposes a hybrid framework that merges a transformer-based subword tokenizer with a linear Support Vector Machine through a TF-IDF vectorization pipeline. Empirical evaluations demonstrate that the proposed architecture balances accuracy and computational efficiency, outperforming sixteen alternative model configurations and establishing itself as an optimal candidate for real-world deployment. This study further led to the development of a web application named ANC v1.0 using Flutter. The application features a responsive interface that provides real-time top-3 news-category predictions with probabilistic confidence scores. By bridging the gap between high-dimensional machine learning and practical web deployment, this work provides a scalable solution for the digital management of Assamese news articles.