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Towards Using Reranking in Hierarchical Classification

Conference paper
Authors Qi Ju
Richard Johansson
Alessandro Moschitti
Published in Proceedings of the Joint ECML/PKDD-PASCAL Workshop on Large-Scale Hierarchical Classification; September 5, 2011; Athens, Greece
Publication year 2011
Published at Department of Swedish
Language en
Keywords datorlingvistik, språkteknologi, textkategorisering, maskininlärning
Subject categories Language Technology (Computational Linguistics), Other information technology


We consider the use of reranking as a way to relax typical in- dependence assumptions often made in hierarchical multilabel classification. Our reranker is based on (i) an algorithm that generates promising k-best classification hypotheses from the output of local binary classifiers that clas- sify nodes of a target tree-shaped hierarchy; and (ii) a tree kernel-based reranker applied to the classification tree associated with the hypotheses above. We carried out a number of experiments with this model on the Reuters corpus: we firstly show the potential of our algorithm by computing the oracle classification accuracy. This demonstrates that there is a signifi- cant room for potential improvement of the hierarchical classifier. Then, we measured the accuracy achieved by the reranker, which shows a significant performance improvement over the baseline.

Page Manager: Webmaster|Last update: 9/11/2012

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