Relation Extraction Based on Relation Label Constraints

• Published in: 2020 IEEE 6th International Conference on Computer and Communications (ICCC) pp. 2166 - 2170
• Main Authors: Lin, Kaihong, Miao, Kehua, Hong, Wenxing, Yuan, Chaoyi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.12.2020
• Subjects: bert; Computational modeling; Correlation; Data mining; Data models; knowledge graphs; Natural language processing; Pipelines; relation extractor; relation matrix constraints; Task analysis
• DOI: 10.1109/ICCC51575.2020.9344883

Knowledge graphs have a significant role in promoting natural language processing tasks, and they have received substantial attention. The relation extractor is a key step in the construction of a knowledge graph, so it is important to improve its performance. However, previous works are mainly based on the pipeline method, which rarely address the problem of overlapping triplets. In addition, the literature does not consider models in which the correlation between relation pairs is addressed, which limits their accuracy. In this paper, we propose a new model called Relation Extraction Based On Relation Label Constraints(RRC) that is based on relation matrix constraints. The subject is extracted in our model in the first step; then, the relation and object are extracted based on the subject information. Each relation is regarded as a vector to assist in the extraction of the relation and object; the vector is used to consider the correlation between the relation vectors. This is used as a constraint to optimize the relation vector. Experiments on two public datasets, NYT and WebNLG, show that this method can perform well.