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
- 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)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.ecnlp-1.10.pdf