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End-to-End Neural Text Classification for Tibetan

  • Nuo Qun
  • Xing Li
  • Xipeng QiuEmail author
  • Xuanjing Huang
Conference paper
  • 1.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10565)

Abstract

As a minority language, Tibetan has received relatively little attention in the field of natural language processing (NLP), especially in current various neural network models. In this paper, we investigate three end-to-end neural models for Tibetan text classification. The experimental results show that the end-to-end models outperform the traditional Tibetan text classification methods. The dataset and codes are available on https://github.com/FudanNLP/Tibetan-Classification.

Keywords

Neural Model Tibetan Word Tibetan Script Fixed-length Vector Representation Segment Words 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We would like to thank the anonymous reviewers for their valuable comments. This work was partially funded by “Everest Scholars” project of Tibet University, National Natural Science Foundation of China (No. 61262086), Autonomous Science and Technology Major Project of the Tibet Autonomous Region Science and Technology.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Information Science and TechnologyTibet UniversityTibetChina
  2. 2.School of Computer ScienceFudan UniversityShanghaiChina

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