Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (4): 466-473.doi: 10.11959/j.issn.2096-6652.202146

• Papers and Reports • Previous Articles     Next Articles

Tibetan entity relation extraction based on multi-level attention fusion mechanism

Like WANG1,2, Yuan SUN1,2, Sisi LIU1,2   

  1. 1 School of Information Engineering, Minzu University of China, Beijing 100081, China
    2 National Language Resource and Monitoring and Research Center of Minority Languages, Minzu University of China, Beijing 100081, China
  • Revised:2021-02-23 Online:2021-12-15 Published:2021-12-01
  • Supported by:
    The National Nature Science Foundation of China(61972436)


Compared with Chinese and English, the training corpus of Tibetan entity relation is smaller, so it is difficult to obtain higher accuracy based on traditional supervised learning methods.And there exists the problem of wrong labels in distant supervision for relation extraction.To solve these problems, the distant supervision method was used to construct the data set of Tibetan entity relation extraction through aligning the knowledge base with texts, which could alleviate the problem of lacking of large-scale corpus in Tibetan.And a Tibetan entity relation extraction model based on multi-level attention fusion mechanism was proposed.The self-attention was added to extract internal features of words in word level.The selective attention mechanism could assign weights of each instance, so as to make full use of informative sentences and reduce weights of noisy instances.Meanwhile, a joint score function was introduced to correct wrong labels, and neural network was combined with support vector machine to extract relations.Experimental results show that the proposed model can effectively improve the accuracy of Tibetan entity relation extraction, and is better than the baseline.

Key words: Tibetan, entity relation extraction, multi-level attention fusion mechanism, support vector machine

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