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Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
2022
Applied Sciences
In recent years, scholars have paid increasing attention to the joint entity and relation extraction. However, the most difficult aspect of joint extraction is extracting overlapping triples. To address this problem, we propose a joint extraction model based on Soft Pruning and GlobalPointer, short for SGNet. In the first place, the BERT pretraining model is used to obtain the text word vector representation with contextual information, and then the local and non-local information of the word
doi:10.3390/app12136361
fatcat:773ukym7c5g55bbvkrsrkei7c4