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.

In recent years, language resources acquired from theWeb are released, and these data improve the performance of applications in several NLP tasks. Although the language resources based on the web page unit are useful in NLP tasks and applications such as knowledge acquisition, document retrieval and document summarization, such language resources are not released so far. In this paper, we propose a data format for results of web page processing, and a search engine infrastructure which makes it possible to share approximately 100 million Japanese web data. By obtaining the web data, NLP researchers are enabled to begin their own processing immediately without analyzing web pages by themselves.