@inproceedings{amigo-delgado-2022-evaluating,
title = "Evaluating Extreme Hierarchical Multi-label Classification",
author = "Amigo, Enrique and
Delgado, Agust{\'\i}n",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.399",
doi = "10.18653/v1/2022.acl-long.399",
pages = "5809--5819",
abstract = "Several natural language processing (NLP) tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification, in which items may be associated with multiple classes from a set of thousands of possible classes organized in a hierarchy and with a highly unbalanced distribution both in terms of class frequency and the number of labels per item. We analyze the state of the art of evaluation metrics based on a set of formal properties and we define an information theoretic based metric inspired by the Information Contrast Model (ICM). Experiments on synthetic data and a case study on real data show the suitability of the ICM for such scenarios.",
}
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%0 Conference Proceedings
%T Evaluating Extreme Hierarchical Multi-label Classification
%A Amigo, Enrique
%A Delgado, Agustín
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F amigo-delgado-2022-evaluating
%X Several natural language processing (NLP) tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification, in which items may be associated with multiple classes from a set of thousands of possible classes organized in a hierarchy and with a highly unbalanced distribution both in terms of class frequency and the number of labels per item. We analyze the state of the art of evaluation metrics based on a set of formal properties and we define an information theoretic based metric inspired by the Information Contrast Model (ICM). Experiments on synthetic data and a case study on real data show the suitability of the ICM for such scenarios.
%R 10.18653/v1/2022.acl-long.399
%U https://aclanthology.org/2022.acl-long.399
%U https://doi.org/10.18653/v1/2022.acl-long.399
%P 5809-5819
Markdown (Informal)
[Evaluating Extreme Hierarchical Multi-label Classification](https://aclanthology.org/2022.acl-long.399) (Amigo & Delgado, ACL 2022)
ACL
- Enrique Amigo and Agustín Delgado. 2022. Evaluating Extreme Hierarchical Multi-label Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5809–5819, Dublin, Ireland. Association for Computational Linguistics.