Propagating Fine-Grained Topic Labels in News Snippets

被引：0
|
作者：
Sarmento, Luis [1 ,2 ]
Nunes, Sergio [2 ]
Teixeira, Jorge [1 ,2 ]
Oliveira, Eugenio [1 ,2 ]
机构：
[1] Univ Porto, Fac Engn, LIACC, Rua Dr Roberto Frias S-N, P-4200465 Oporto, Portugal
[2] Univ Porto, Fac Engn, DEI, P-4200465 Oporto, Portugal
来源：
2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3 | 2009年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We propose an unsupervised method for propagating automatically extracted fine-grained topic labels among news items to improve their topic description for subsequent text classification procedure. This method compares vector representations of news items and assigns to each news item the label of its closest neighbour with a different topic label. Results obtained show that high precision can be achieved in propagating the top ranked topic label, and that 2-gram and 3-gram feature representations optimize the precision.
引用
收藏
页码：515 / +
页数：2
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