基于预训练模型和图卷积网络的中文短文本实体链接

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Abstract Short text entity linking can relies on local short text information and knowledge base due to the lack of global topic information. This paper proposes the concept of short text interaction graph (STIG) and a double stage training strategy. The Bert is used to extract the multi-granularity features between local short text and candidate entities, and the graph convolution mechanism is used on the short text interaction graph. To alleviate the degradation of graph convolution caused by mean pooling, a method is further proposed to compress the feature of nodes and edges information in interaction graph into a dense vector. Experiments on CCKS2020 entity linking dataset show the effectiveness of the proposed method.

Key words： entity linking BERT graph convolutional neural network

Received: 17 November 2020

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	Articles by authors
	GUO Shiwei
	MA Bo
	MA Yupeng
	YANG Yating

Cite this article:

GUO Shiwei,MA Bo,MA Yupeng, et al. Chinese Short Text Entity Linking Based on BERT and GCN[J]. , 2022, 36(12): 104-114.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I12/104

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