Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models

EACL 2017 · Y Xia, i, Aaron Levine, Pradipto Das, Giuseppe Di Fabbrizio, Keiji Shinzato, Ankur Datta ·

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.

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