Deep Hierarchical Classification for Category Prediction in E-commerce System

Dehong Gao


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.
Anthology ID:
2020.ecnlp-1.10
Volume:
Proceedings of the 3rd Workshop on e-Commerce and NLP
Month:
July
Year:
2020
Address:
Seattle, WA, USA
Editors:
Shervin Malmasi, Surya Kallumadi, Nicola Ueffing, Oleg Rokhlenko, Eugene Agichtein, Ido Guy
Venue:
ECNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
64–68
Language:
URL:
https://aclanthology.org/2020.ecnlp-1.10
DOI:
10.18653/v1/2020.ecnlp-1.10
Bibkey:
Cite (ACL):
Dehong Gao. 2020. Deep Hierarchical Classification for Category Prediction in E-commerce System. In Proceedings of the 3rd Workshop on e-Commerce and NLP, pages 64–68, Seattle, WA, USA. Association for Computational Linguistics.
Cite (Informal):
Deep Hierarchical Classification for Category Prediction in E-commerce System (Gao, ECNLP 2020)
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PDF:
https://aclanthology.org/2020.ecnlp-1.10.pdf
Video:
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