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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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
Pages223-228
Number of pages6
ISBN (Electronic)9782875870742
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Virtual, Bruges, Belgium
Duration: 2 Oct 20204 Oct 2020
Conference number: 28

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN
CountryBelgium
CityBruges
Period02/10/202004/10/2020
OtherVirtual

Fingerprint Dive into the research topics of 'Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification'. Together they form a unique fingerprint.

Cite this