Integration of Relation Filtering and Multi-Task Learning in GlobalPointer for Entity and Relation Extraction

• Published in: Applied sciences Vol. 14; no. 15; p. 6832
• Main Authors: Liu, Bin, Tao, Jialin, Chen, Wanyuan, Zhang, Yijie, Chen, Min, He, Lei, Tang, Dan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Artificial intelligence; Computational linguistics; joint entity and relation extraction; knowledge graph; Knowledge representation; Labeling; Language processing; Methods; multi-task learning; Natural language interfaces; Neural networks; overlapping triplets; Triplets; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14156832

The rise of knowledge graphs has been instrumental in advancing artificial intelligence (AI) research. Extracting entity and relation triples from unstructured text is crucial for the construction of knowledge graphs. However, Chinese text has a complex grammatical structure, which may lead to the problem of overlapping entities. Previous pipeline models have struggled to address such overlap problems effectively, while joint models require entity annotations for each predefined relation in the set, which results in redundant relations. In addition, the traditional models often lead to task imbalance by overlooking the differences between tasks. To tackle these challenges, this research proposes a global pointer network based on relation prediction and loss function improvement (GPRL) for joint extraction of entities and relations. Experimental evaluations on the publicly available Chinese datasets DuIE2.0 and CMeIE demonstrate that the GPRL model achieves a 1.2–26.1% improvement in F1 score compared with baseline models. Further, experiments of overlapping classification conducted on CMeIE have also verified the effectiveness of overlapping triad extraction and ablation experiments. The model is helpful in identifying entities and relations accurately and can reduce redundancy by leveraging relation filtering and the global pointer network. In addition, the incorporation of a multi-task learning framework balances the loss functions of multiple tasks and enhances task interactions.