Paper: Modeling Topic Dependencies in Hierarchical Text Categorization

ACL ID P12-1080
Title Modeling Topic Dependencies in Hierarchical Text Categorization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

In this paper, we encode topic dependencies in hierarchical multi-label Text Categoriza- tion (TC) by means of rerankers. We rep- resent reranking hypotheses with several in- novative kernels considering both the struc- ture of the hierarchy and the probability of nodes. Additionally, to better investigate the role of category relationships, we consider two interesting cases: (i) traditional schemes in which node-fathers include all the documents of their child-categories; and (ii) more gen- eral schemes, in which children can include documents not belonging to their fathers. The extensive experimentation on Reuters Corpus Volume 1 shows that our rerankers inject ef- fective structural semantic dependencies in multi-classifiers and significantly outperform the state-of-the-art.