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
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
663–668
Language:
URL:
https://aclanthology.org/E17-2105
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/E17-2105.pdf