Tibetan Entity Relation Extraction Based on Joint Model
XIA Tianci1,2, SUN Yuan1,2
1.School of Information Engineering, Minzu University of China, Beijing 100081, China; 2.Minority Languages Branch, National Language Resource and Monitoring Research Center, Minzu University of China, Beijing 100081, China
Abstract：Extracting the entities and the relationship between them from unstructured texts is a challenging issue. This paper applies the joint model in Tibetan to perform the entity identification and relation extraction at the same time. An end-to-end sequence labelling framework of BiLSTM is adopted, and the POS information is integrated to enhance the performance. It is also demonstrated that the character-level processing method is more effective in Tibetan than the word-level processing. The experimental results show that the method improves the accuracy by 30%～40%, compared the SVM and LR.
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2018, Vol. 32 
