@inproceedings{gao-2020-deep,
title = "Deep Hierarchical Classification for Category Prediction in {E}-commerce System",
author = "Gao, Dehong",
editor = "Malmasi, Shervin and
Kallumadi, Surya and
Ueffing, Nicola and
Rokhlenko, Oleg and
Agichtein, Eugene and
Guy, Ido",
booktitle = "Proceedings of the 3rd Workshop on e-Commerce and NLP",
month = jul,
year = "2020",
address = "Seattle, WA, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.ecnlp-1.10",
doi = "10.18653/v1/2020.ecnlp-1.10",
pages = "64--68",
abstract = "In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy.",
}
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%0 Conference Proceedings
%T Deep Hierarchical Classification for Category Prediction in E-commerce System
%A Gao, Dehong
%Y Malmasi, Shervin
%Y Kallumadi, Surya
%Y Ueffing, Nicola
%Y Rokhlenko, Oleg
%Y Agichtein, Eugene
%Y Guy, Ido
%S Proceedings of the 3rd Workshop on e-Commerce and NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, WA, USA
%F gao-2020-deep
%X In e-commerce system, category prediction is to automatically predict categories of given texts. Different from traditional classification where there are no relations between classes, category prediction is reckoned as a standard hierarchical classification problem since categories are usually organized as a hierarchical tree. In this paper, we address hierarchical category prediction. We propose a Deep Hierarchical Classification framework, which incorporates the multi-scale hierarchical information in neural networks and introduces a representation sharing strategy according to the category tree. We also define a novel combined loss function to punish hierarchical prediction losses. The evaluation shows that the proposed approach outperforms existing approaches in accuracy.
%R 10.18653/v1/2020.ecnlp-1.10
%U https://aclanthology.org/2020.ecnlp-1.10
%U https://doi.org/10.18653/v1/2020.ecnlp-1.10
%P 64-68
Markdown (Informal)
[Deep Hierarchical Classification for Category Prediction in E-commerce System](https://aclanthology.org/2020.ecnlp-1.10) (Gao, ECNLP 2020)
ACL