Incremental reranking for hierarchical text classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The top-down method is efficient and commonly used in hierarchical text classification. Its main drawback is the error propagation from the higher to the lower nodes. To address this issue we propose an efficient incremental reranking model of the top-down classifier decisions. We build a multiclassifier for each hierarchy node, constituted by the latter and its children. Then we generate several classification hypotheses with such classifiers and rerank them to select the best one. Our rerankers exploit category dependencies, which allow them to recover from the multiclassifier errors whereas their application in top-down fashion results in high efficiency. The experimentation on Reuters Corpus Volume 1 (RCV1) shows that our incremental reranking is as accurate as global rerankers but at least one magnitude order faster.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages726-729
Number of pages4
Volume7814 LNCS
DOIs
Publication statusPublished - 2 Apr 2013
Externally publishedYes
Event35th European Conference on Information Retrieval, ECIR 2013 - Moscow, Russian Federation
Duration: 24 Mar 201327 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7814 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other35th European Conference on Information Retrieval, ECIR 2013
CountryRussian Federation
CityMoscow
Period24/3/1327/3/13

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Ju, Q., & Moschitti, A. (2013). Incremental reranking for hierarchical text classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7814 LNCS, pp. 726-729). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7814 LNCS). https://doi.org/10.1007/978-3-642-36973-5_70