MacBERT-based Joint Extraction of Carbon Neutrality Entities and Relationships
[Objective]To achieve semantic association mining between carbon-neutral data and improve the overall accuracy of triplet extraction,this paper proposes a HmBER model for joint entity-relation extraction based on MacBERT.[Methods]In the HmBER model,we enhanced the performance in joint extraction of carbon-neutral entity relationships through similarity measurement,auxiliary training with entity boundaries,and introducing entity category features in relation extraction.[Results]Compared with Multi-head,CasRel,SpERT,and STER models,the F1 score of the HmBER model on the carbon-neutral dataset achieved an average improvement by 2.39％and 13.84％,respectively.[Limitations]The data processed by this method requires inference of sentence meaning to derive entity-relation joint extraction results and deeper latent semantic mining was not performed.[Conclusions]The HmBER model effectively addresses data annotation omission and entity boundary errors,providing a highly accurate approach for entity-relation joint extraction.
Entity and Relation ExtractionCarbon NeutralityBoundary ErrorMissing Label
NETL
NSTL
万方数据
