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

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.

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Mohammadnia Qaraei, M, Khandagale, S & Babbar, R 2020, Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification. in ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com, pp. 223-228, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 02/10/2020. < https://www.esann.org/sites/default/files/proceedings/2020/ES2020-207.pdf >

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https://urn.fi/URN:NBN:fi:aalto-2021120110556

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[article-cris] Perustieteiden korkeakoulu / SCI

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

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