Complex relation extraction from health aquaculture standards based on an improved BiRTE model
SONG Qishu，YU Hong*，QIAO Shihan，LUO Xuan，LI Guangyu，SHAO Liming，ZHANG Sijia
1.College of Information Engineering，Dalian Ocean University，Dalian 116023，China；2.Dalian Key Laboratory of Smart Fisheries，Dalian 116023，China；3.Key Laboratory of Environment Controlled Aquaculture (Dalian Ocean University)，Ministry of Education，Dalian 116023，China；4.Key Laboratory of Marine Information Technology of Liaoning Province，Dalian 116023，China
Abstract: A complex relationship extraction method for health aquaculture standards is proposed to address issues such as inaccurate recognition of domain-specific nouns and the complexity of semantics hindering entity relationship extraction based on an improved BiRTE model. The BiRTE model, which reduces error propagation through bidirectional extraction and exhibits strong relationship extraction capabilities, was adopted as the foundational model. To enhance the model’s information-extracting ability from texts of fisheries standard files, RoBERTa was used as the encoder encoding domain-specific nouns in fisheries standard files using whole-word masking and dynamic masking, enriching word vector information and enhancing feature representation. Thus, the Self-Attention is integrated to combine entity features and relationship features, focusing on strengthening the connection between entity extraction and relation prediction, thereby improving the accuracy of relation extraction. It was found that the proposed model achieved precision of 95.9%, recall of 95.4%, and F1 scores of 95.7% from the extraction of complex relationships in fisheries standards, representing an improvement of 4.2%, 3.1%, and 3.8%, respectively, compared to the original model. The finding indicates that the proposed improved BiRTE-based model, as an effective method for extracting complex relationships in fishing standards, can effectively address the problems of inaccurate identification of proper nouns and difficulty in extracting entity relationships due to semantic complexity in the field of fishing standard text relation extraction.
2024, Vol. 39 
