| Title |
Auto-classification of Government Department-specific News Articles |
| ID_Doc |
79231 |
| Authors |
Lee, SM; Ryu, SE; Ahn, SJ |
| Title |
Auto-classification of Government Department-specific News Articles |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/CSCI49370.2019.00286 |
| Abstract |
The purpose of this study is to propose an unsupervised learning-based method for automatic classification of news articles usilig a dictionary, mcorporatiiig the attributes of each admmistrative department. The results of auto-classification of news articles for individual departments showed 71% accuracy. A classification technique using unsupervised learning may be utilized to automatically classify documents without labels when gathering policy issues for individual administrative departments. |
| Author Keywords |
unsupervised karning; auto-class fication; document classification; administrative agency; policy issue |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000569996300279 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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