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• W2163081268 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." @default.
• W2163081268 created "2016-06-24" @default.
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• W2163081268 date "2012-07-08" @default.
• W2163081268 modified "2023-09-23" @default.
• W2163081268 title "Modeling Topic Dependencies in Hierarchical Text Categorization" @default.
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