Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Text Classification of News Articles Using Machine Learning on Low-resourced Language: Tigrigna

Type de record:

paper
Créateur:
AweSheEshAbd
Éditeur:
IEEE
Hôte:

Visit

doi.org

Tasks

news classificationtext classificationtopic classification

Languages

Tigrigna

Licenses

https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.htmlhttps://doi.org/10.15223/policy-029https://doi.org/10.15223/policy-037

Similaires

Classifying News Articles on Nigerian Newspapers Using Machine LearningPOLITICAL STANCE DETECTION AND CLASSIFICATION ON TIGRIGNA TEXT USING DEEP LEARNING APPROACHESPre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe LanguageAfaan Oromo News Text Classification Using Deep LearningClassification and Detection of Amharic Language Fake News on Social Media Using Machine Learning ApproachAbstractive text summarization of low-resourced languages using deep learning

Classifying News Articles on Nigerian Newspapers Using Machine Learning

With the escalation of news sources, it presents challenge for a reviewer to find the exact newspape

POLITICAL STANCE DETECTION AND CLASSIFICATION ON TIGRIGNA TEXT USING DEEP LEARNING APPROACHES

The rise of social media has transformed public discourse, providing platforms for individuals to ex

Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

Despite a few attempts to automatically crawl Ewe text from online news portals and magazines, the A

Afaan Oromo News Text Classification Using Deep Learning

Abstract The recent development of the internet has significan

Classification and Detection of Amharic Language Fake News on Social Media Using Machine Learning Approach

The pervasive idea of web-based media stages brought about a lot of sight and sound information in i

Abstractive text summarization of low-resourced languages using deep learning

Background Humans must be able to cope with the huge amounts of information produce