Modeling Topic Dependencies in Hierarchical Text Categorization
Alessandro Moschitti, Qi Ju, Richard Johansson · Institutional Research Information System (Università degli Studi di Trento) · 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.