Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement.

ABSTRACT: In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lac

SUBMITTER: Cheng Q 

PROVIDER: S-EPMC11623068 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature