Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification

Mohammadreza Qaraei, Sujay Khandagale, Rohit Babbar

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

Abstract

Extreme Multi-label Text Classification (XMTC) refers to supervised learning of a classifier which can predict a small subset of relevant labels for a document from an extremely large set. Even though deep learning algorithms have surpassed linear and kernel methods for most natural language processing tasks over the last decade; recent works show that state-of-the-art deep learning methods can only barely manage to work as well as a linear classifier for the XMTC task. The goal of this work is twofold: (i) to investigate the reasons for the comparable performance of these two strands of methods for XMTC, and (ii) to document this observation explicitly, as the efficacy of linear classifiers in this regime, has been ignored in many relevant recent works.

Original languageEnglish
Title of host publicationESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PublisherESANN (i6doc.com)
Pages223-228
Number of pages6
ISBN (Electronic)9782875870742
Publication statusPublished - 2 Oct 2020
Event28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020 - Virtual, Online, Belgium
Duration: 2 Oct 20204 Oct 2020

Publication series

NameESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020
Country/TerritoryBelgium
CityVirtual, Online
Period2/10/204/10/20

Bibliographical note

Publisher Copyright:
© ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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