[Purpose/significance] This paper studies the self-organization method of the recessive diffusion path of science and technology policy, excavates the deep semantic information contained in the text of science and technology policy, makes the tacit knowledge explicit, and provides reference for researchers to expand and enrich the research of policy diffusion path. [Method/process] This paper combines the formal semantics and content semantics of science and technology policy texts to deal with the structural processing and deep mining of policy texts, fully analyzes the poli? cy text resources, and extracts the features contained in the science and technology policy texts, including the automat? ic acquisition and indexing technology of concepts and relationships, network representation learning, mining the im? plicit structural information in the science and technology policy texts, and using the deep learning method of BiLSTMCRF model to realize the automatic acquisition and indexing of concepts. The concepts and relationships of many sci? ence and technology policy texts are formed into concept relationship pairs, and dense vector representations of each node are found by means of representation learning. [Result/conclusion] Through the experimental verification, it is proved that the self- organization method of the hidden path of science and technology policy diffusion based on the characteristics of the hidden path in this paper is effective, which expands the method of policy research to a certain ex? tent and provides a reference for researchers in policy diffusion research.
Xu Qiankun
,
Liu Yao
. Research on Self-organization of Hidden Diffusion Path of Science and Technology Policy[J]. Information and Documentation Services, 2022
, 43(1)
: 61
-70
.
DOI: 10.12154/j.qbzlgz.2022.01.005
