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.
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 language | English |
---|---|
Title of host publication | ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Pages | 223-228 |
Number of pages | 6 |
ISBN (Electronic) | 9782875870742 |
Publication status | Published - 2020 |
MoE publication type | A4 Article in a conference publication |
Event | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Virtual, Bruges, Belgium Duration: 2 Oct 2020 → 4 Oct 2020 Conference number: 28 |
Conference
Conference | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
---|---|
Abbreviated title | ESANN |
Country | Belgium |
City | Bruges |
Period | 02/10/2020 → 04/10/2020 |
Other | Virtual |