Dual Set Prediction Networks Based Joint Extraction of Entity and Relation

• Published in: Jisuanji kexue yu tansuo Vol. 17; no. 7; pp. 1690 - 1699
• Main Author: PENG Yanfei, WANG Ruihua, ZHANG Ruisi
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.07.2023
• Subjects: joint extraction of entity and relation; dual set prediction networks; entities filter; parallel decoding
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2111103

The entity and relation extraction task, which is the technical source of constructing and updating large-scale knowledge graph, aims to identify the relationship between entities from unstructured text. Among the existing joint extraction methods of entity and relation, parallel decoding of tuples efficiently generates tuples by set prediction. However, this method ignores the interaction between entity and relationship, and entity subject and object, resulting in the generation of invalid tuples. To address this problem, this paper proposes a joint extraction model of entity and relation based on dual set prediction networks. To enhance the interaction between relationships and entities, a dual set prediction network is used to decode the tuples in parallel, and the entity information and relationship types in the tuples are generated sequentially. The first set prediction network models the set of tuples and decodes the subject-object information in the tuple. The second set prediction network models the s