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
Venues:
ACL | ECNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
64–68
Language:
URL:
https://www.aclweb.org/anthology/2020.ecnlp-1.10
DOI:
10.18653/v1/2020.ecnlp-1.10
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ecnlp-1.10.pdf
Video:
 http://slideslive.com/38931246