E2CNN: entity-type-enriched cascaded neural network for Chinese financial relation extraction
Mengfan Li, Xuanhua Shi, Chenqi Qiao, Xiao Huang, Weihao Wang, Yao Wan, Teng Zhang, Hai Jin · Frontiers of Computer Science · 2025
Abstract Knowledge Graphs (KGs) are pivotal for effectively organizing and managing structured information across various applications. Financial KGs have been successfully employed in advancing applications such as audit, anti-fraud, and anti-money laundering. Despite their success, the construction of Chinese financial KGs has seen limited research due to the complex semantics. A significant challenge is the overlap triples problem, where entities feature in multiple relations within a sentence, hampering extraction accuracy–more than 39% of the triples in Chinese datasets exhibit the overlap triples. To address this, we propose the Entity-type-Enriched Cascaded Neural Network (E 2 CNN), leveraging special tokens for entity boundaries and types. E 2 CNN ensures consistency in entity types and excludes specific relations, mitigating overlap triple problems and enhancing relation extraction. Besides, we introduce the available Chinese financial dataset F in C orpus .CN, annotated from annual reports of 2,000 companies, containing 48,389 entities and 23,368 triples. Experimental results on the DUIE dataset and F in C orpus .CN underscore E 2 CNN’s superiority over state-of-the-art models.