Hierarchical Text Classification with Latent Concepts

Xipeng Qiu,  Xuanjing Huang,  Zhao Liu,  Jinlong Zhou
Fudan University


Abstract

Recently, hierarchical text classification has become an active research topic. The essential idea is that the descendant classes can share the information of the ancestor classes in a predefined taxonomy. In this paper, we claim that each class has several latent concepts and its subclasses share information with these different concepts respectively. Then, we propose a variant Passive-Aggressive (PA) algorithm for hierarchical text classification with latent concepts. Experimental results show that the performance of our algorithm is competitive with the recently proposed hierarchical classification algorithms.




Full paper: http://www.aclweb.org/anthology/P/P11/P11-2105.pdf