Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions release_que7lt6h35al7mkn4zzhceesba

by Ruohong Zhang, Yau-Shian Wang, Yiming Yang, Donghan Yu, Tom Vu, Likun Lei

Released as a article .

2022  

Abstract

Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated with the long tail of rare labels in highly skewed distributions. This paper addresses the challenge of tail label prediction by proposing a novel approach, which combines the effectiveness of a trained bag-of-words (BoW) classifier in generating informative label descriptions under severe data scarce conditions, and the power of neural embedding based retrieval models in mapping input documents (as queries) to relevant label descriptions. The proposed approach achieves state-of-the-art performance on XMTC benchmark datasets and significantly outperforms the best methods so far in the tail label prediction. We also provide a theoretical analysis for relating the BoW and neural models w.r.t. performance lower bound.
In text/plain format

Archived Files and Locations

application/pdf  544.0 kB
file_dabj4nvcvbhb5mfn6zej2w6uau
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-04-02
Version   v1
Language   en ?
arXiv  2204.00958v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 8e9e94f9-8ce3-4db2-b393-1059f5a462e9
API URL: JSON