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.

Modeling Topic Dependencies in Hierarchical Text Categorization

Moschitti, Alessandro;Ju, Qi;
2012-01-01

Abstract

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.
2012
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Jeju Island, Korea
The Association for Computer Linguistics
Moschitti, Alessandro; Ju, Qi; R., Johansson
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/95263
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