Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700064-5.doi: 10.11896/jsjkx.230700064

• Artificial Intelligenc • Previous Articles     Next Articles

Study on Tibetan Short Text Classification Based on DAN and FastText

LI Guo1,2, CHEN Chen1,2, YANG Jing1,3, QUN Nuo1   

  1. 1 School of Information Science and Technology,Tibet University,Lhasa 850000,China
    2 Engineering Research Center of Tibetan Information Technology Ministry of Education,Tibet University,Lhasa 850000,China
    3 School of Cyber Science and Engineering,Sichuan University,Chengdu 610000,China
  • Published:2024-06-06
  • About author:LI Gu,born in 1994,postgraduate.His main research interest includs natural language processing.
    YANG Jing,born in 1980,professor.His main research interests include cyberspace security and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61872254,62162057).

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Abstract: As Tibetan information continues to be integrated into social life,more and more Tibetan short text data is available on online platforms.Aiming at the low classification performance of traditional classification methods on Tibetan short texts,a Tibetan short text classification model based on DAN-FastText is proposed.The model uses the FastText network to perform unsupervised training on a large-scale Tibetan corpus to obtain the pre-trained Tibetan syllabic vector set,uses the pre-trained syllable vector set to convert the Tibetan short text information into syllable vector,sends the syllable vector into the deep averaging networks(DAN) network and fuses the sentence vector features trained by the FastText network in the output stage,and finally completes the classification through the fully connected layer and the softmax layer.On the publicly available tibetan news classification corpus(TNCC) news headline dataset,Macro-F1 is 64.53%,which is 2.81% higher than that of the TiBERT model and 6.14% higher than that GCN model,and the fusion model has a better Tibetan short text classification effect.

Key words: Tibetan short text classification, Feature fusion, Deep averaging networks, Fast text

CLC Number: 

  • TP391.1
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