Multi-Level Text Importance Classification Architecture Based on Deep Learning

被引：0

作者：

Huang, Meizhen ^{[1
]}

Su, Jinshu ^{[1
]}

Liao, Zhong ^{[2
]}

Chen, Shuhui ^{[1
]}

Wei, Ziling ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Comp, Changsha, Peoples R China

[2] Hunan Prov Key Lab Media Fus Content Aware & Secu, Yueyang, Peoples R China

来源：

PROCEEDINGS OF THE 6TH ASIA-PACIFIC WORKSHOP ON NETWORKING, APNET 2022 | 2022年

关键词：

D O I：

10.1145/3542637.3543702

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the era of information explosion, the Internet is full of spam and false information, making it more difficult for people to obtain effective information. Since text data is the main carrier for disseminating information and knowledge, we propose a multi-level text importance classification architecture based on deep learning to enable Internet users to quickly and accurately access text content of interest. Experiments demonstrate that the proposed architecture can achieve a good performance.

引用

页码：87 / 89

页数：3