Uzbek News Categorization using Word Embeddings and Convolutional Neural Networks
Ilyos Rabbimov, Sami Kobilov, Iosif Mporas
Abstract
The rapid growth of online news belonging to different categories is causing users to spend a lot of time and effort searching for relevant and important news. Text categorization has a great significance in information retrieval and natural language processing where unstructured text can be organized into predefined categories. In this paper we investigate Uzbek news categorization using a convolution neural network and four word embedding models. We obtain two new word embeddings for Uzbek and present them in the Uzbek news categorization task.
Topics & Concepts
UzbekComputer scienceConvolutional neural networkCategorizationWord (group theory)Artificial intelligenceNatural language processingText categorizationLinguisticsPhilosophyText and Document Classification TechnologiesAdvanced Text Analysis TechniquesTopic Modeling