A joint model for entity and relation extraction based on BERT

• Published in: Neural computing & applications Vol. 34; no. 5; pp. 3471 - 3481
• Main Authors: Qiao, Bo, Zou, Zhuoyang, Huang, Yu, Fang, Kui, Zhu, Xinghui, Chen, Yiming
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Deep learning; Image Processing and Computer Vision; Knowledge; Knowledge representation; Language; Natural language processing; Neural networks; Probability and Statistics in Computer Science; Semantics; Special Issue on Multi-modal Information Learning and Analytics on Big Data; Standard data; Relation extraction; Agricultural knowledge graph; BERT; Named entity recognition; Joint extraction
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-05815-z

In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the internet and artificial intelligence. However, there is no mature knowledge graph in the field of agriculture, so it is a great significance study on the construction technology of agricultural knowledge graph. Named entity recognition and relation extraction are key steps in the construction of knowledge graph. In this paper, based on the joint extraction model LSTM-LSTM-Bias brought in BERT pre-training language model to proposed a agricultural entity relationship joint extraction model BERT-BILSTM-LSTM which is applied to the standard data set NYT and self-built agricultural data set AgriRelation. Experimental results showed that the model can effectively extracted the relationship between agricultural entities and entities.