Label-Guided Learning for Item Categorization in e-Commerce

Lei Chen, Hirokazu Miyake


Abstract
Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from a major e-commerce company in Japan. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.
Anthology ID:
2021.naacl-industry.37
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
Month:
June
Year:
2021
Address:
Online
Editors:
Young-bum Kim, Yunyao Li, Owen Rambow
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
296–303
Language:
URL:
https://aclanthology.org/2021.naacl-industry.37
DOI:
10.18653/v1/2021.naacl-industry.37
Bibkey:
Cite (ACL):
Lei Chen and Hirokazu Miyake. 2021. Label-Guided Learning for Item Categorization in e-Commerce. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 296–303, Online. Association for Computational Linguistics.
Cite (Informal):
Label-Guided Learning for Item Categorization in e-Commerce (Chen & Miyake, NAACL 2021)
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PDF:
https://aclanthology.org/2021.naacl-industry.37.pdf
Video:
 https://aclanthology.org/2021.naacl-industry.37.mp4