Research on Tibetan Text Classification Method Based on Neural Network

Abstract

Text categorization is an important task in natural language processing, and it has a wide range of applications in real life. In this paper, two N-Gram feature models (MLP, FastText) and two sequential models (sepCNN, Bi-LSTM) are used to study the automatic classification for Tibetan text based on syllables and vocabulary. The experiment on Tibetan language data collected by China Tibet News Network shows that the classification accuracy is about 85%.