SCIENCE@home

Your Extreme Multi-label Classifier is Secretly a Hierarchical Text Classifier for Free

Nerijus Bertalis,Paul Granse,Ferhat Gul,Florian Hauss,Leon Menkel,D. Schuler,Tom Speier,Lukas Galke Poech,A. Scherp

Published 2024 in Unknown venue

ABSTRACT

Assigning a set of labels to a given text is a classification problem with many real-world applications, such as recommender systems. Two separate research streams address this issue. Hierarchical Text Classification (HTC) focuses on datasets with label pools of hundreds of entries, accompanied by a semantic label hierarchy. In contrast, eXtreme Multi-Label Text Classification (XML) considers very large sets of labels with up to millions of entries but without an explicit hierarchy. In XML methods, it is common to construct an artificial hierarchy in order to deal with the large label space before or during the training process. Here, we investigate how state-of-the-art HTC models perform when trained and tested on XML datasets and vice versa using three benchmark datasets from each of the two streams. Our results demonstrate that XML models, with their internally constructed hierarchy, are very effective HTC models. HTC models, on the other hand, are not equipped to handle the sheer label set size of XML datasets and achieve poor transfer results. We further argue that for a fair comparison in HTC and XML, more than one metric like F1 should be used but complemented with P@k and R-Precision.

PUBLICATION RECORD

  • Publication year

    2024

  • Venue

    Unknown venue

  • Publication date

    2024-11-20

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 2411.13687
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-42 of 42 references · Page 1 of 1

CITED BY