A Novel Joint Entity Relation Extraction Based on Capsule Network and Part-of-Speech Weighting

Abstract

With the development of science and technology, science and technology policies are increasing year by year. Science and technology policies are literature existing in the form of texts, which are characterized by rigorous structure, clear hierarchy, and standard language. Mining template information from policies can optimize data templates and improve the efficiency of recommending data to users. This paper proposes a joint entity relation extraction model based on capsule networks and part-of-speech weighting. In order to learn more feature information from word vector, capsule network based on bidirectional gated cyclic unit is used to replace the traditional convolutional neural network. In view of the phenomenon of imperfect semantic expression of word vector, part-of-speech features are added to enrich text information. Meanwhile, in order to solve the weight distribution problem of word features and part-of-speech features, an artificial fish swarm algorithm is proposed to optimize the two feature weights by iterative optimization, and the effectiveness of the proposed model is proved by experiments.