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Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H. and Xu, B. (2016) Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Vol. 2: Short Papers, 207-212. https://doi.org/10.18653/v1/P16-2034

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• TITLE: An End-to-End Method for Joint Extraction of Tibetan Entity Relations

  AUTHORS: Yuan Sun, Sisi Liu, Tianci Xia, Xiaobing Zhao

  KEYWORDS: End-to-End Model, Tibetan Entity Relation, Joint Method, Character-Level Processing

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.9, September 30, 2021

  ABSTRACT: Entity relation extraction is to find entities and relations from unstructured texts, which is beneficial to the applications of knowledge graphs and question answering systems. The traditional methods handle this task in a pipelined manner which extracts the entities first and then recognizes their relations. This framework may lead to error delivery. In order to tackle this problem, this paper proposes an end-to-end method for joint extraction of Tibetan entity relations which can extract entities and relations at the same time. According to the Tibetan spelling characteristics, this paper processes the Tibetan corpus by word-level and character-level respectively. Combined with part of speech tagging, we use the end-to-end model to convert the entity relation extraction task to the tagging problem. Finally, the experimental results show that the proposed method is better than the baseline.