@InProceedings{toyama-miwa-sasaki:2017:I17-2,
  author    = {Toyama, Yota  and  Miwa, Makoto  and  Sasaki, Yutaka},
  title     = {Utilizing Visual Forms of Japanese Characters for Neural Review Classification},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {378--382},
  abstract  = {We propose a novel method that exploits visual information of ideograms and
	logograms in analyzing Japanese review documents.  Our method first converts
	font images of Japanese characters into character embeddings using
	convolutional neural networks. It then constructs document embeddings from the
	character embeddings based on Hierarchical Attention Networks, which represent
	the documents based on attention mechanisms from a character level to a
	sentence level. The document embeddings are finally used to predict the labels
	of documents. Our method provides a way to exploit visual features of
	characters in languages with ideograms and logograms. In the experiments, our
	method achieved an accuracy comparable to a character embedding-based model
	while our method has much fewer parameters since it does not need to keep
	embeddings of thousands of characters.},
  url       = {http://www.aclweb.org/anthology/I17-2064}
}