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Learning to Rank from Structures in Hierarchical Text Classification

Conference paper
Authors Qi Ju
Alessandro Moschitti
Richard Johansson
Published in Advances in Information Retrieval; 35th European Conference on IR Research, ECIR 2013, Moscow, Russia, March 24-27, 2013; P. Serdyukov et al. (ed)
Volume Lecture Notes in Computer Science 7814
Pages 183-194
ISBN 978-3-642-36972-8
ISSN 0302-9743
Publication year 2013
Published at Department of Swedish
Pages 183-194
Language en
Links link.springer.com/chapter/10.1007%2...
Keywords datorlingvistik, språkteknologi, informationssökning, maskininlärning, textkategorisering
Subject categories Information processing, Language Technology (Computational Linguistics), Informatics, computer and systems science

Abstract

In this paper, we model learning to rank algorithms based on structural dependencies in hierarchical multi-label text categorization (TC). Our method uses the classification probability of the binary classifiers of a standard top-down approach to generate k-best hypotheses. The latter are generated according to their global probability while at the same time satisfy the structural constraints between father and children nodes. The rank is then refined using Support Vector Machines and tree kernels applied to a structural representation of hypotheses, i.e., a hierarchy tree in which the outcome of binary one-vs-all classifiers is directly marked in its nodes. Our extensive experiments on the whole Reuters Corpus Volume 1 show that our models significantly improve over the state of the art in TC, thanks to the use of structural dependecies.

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