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	title        = {Modeling Topic Dependencies in Hierarchical Text Categorization},
	abstract     = {In this paper, we encode topic dependencies in hierarchical multi-label Text Categorization (TC) by means of rerankers. We represent reranking hypotheses with several innovative kernels considering both the structure 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 general 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 effective structural semantic dependencies in multi-classifiers and significantly outperform the state of the art.},
	booktitle    = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012); Jeju, Korea; July 8-14},
	author       = {Moschitti, Alessandro and Ju, Qi and Johansson, Richard},
	year         = {2012},
	pages        = {759--767},