基于神经网络的藏文文本分类方法研究

2019 International Conference on Asian Language Processing (IALP) Pub Date : 2019-11-01 DOI:10.1109/IALP48816.2019.9037706

Zhensong Li, Jie Zhu, Zhixiang Luo, Saihu Liu

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引用次数: 1

摘要

文本分类是自然语言处理中的一项重要任务，在现实生活中有着广泛的应用。本文采用两个N-Gram特征模型(MLP、FastText)和两个序列模型(sepCNN、Bi-LSTM)对基于音节和词汇的藏文文本自动分类进行了研究。对中国西藏新闻网收集的藏语数据进行实验，结果表明，该方法的分类准确率约为85%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Research on Tibetan Text Classification Method Based on Neural Network

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%.

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2019 International Conference on Asian Language Processing (IALP)

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