Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models
Yandi Xia, Aaron Levine, Pradipto Das, Giuseppe Di Fabbrizio, Keiji Shinzato, Ankur Datta
Abstract
We propose a variant of Convolutional Neural Network (CNN) models, the Attention CNN (ACNN); for large-scale categorization of millions of Japanese items into thirty-five product categories. Compared to a state-of-the-art Gradient Boosted Tree (GBT) classifier, the proposed model reduces training time from three weeks to three days while maintaining more than 96% accuracy. Additionally, our proposed model characterizes products by imputing attentive focus on word tokens in a language agnostic way. The attention words have been observed to be semantically highly correlated with the predicted categories and give us a choice of automatic feature extraction for downstream processing.- Anthology ID:
- E17-2105
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 663–668
- Language:
- URL:
- https://aclanthology.org/E17-2105
- DOI:
- Cite (ACL):
- Yandi Xia, Aaron Levine, Pradipto Das, Giuseppe Di Fabbrizio, Keiji Shinzato, and Ankur Datta. 2017. Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 663–668, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models (Xia et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-2105.pdf