  For analysing and/or understanding languages having no word boundaries based
on morphological analysis such as Japanese, Chinese, and Thai, it is desirable
to perform appropriate word segmentation before word embeddings. But it is
inherently difficult in these languages. In recent years, various language
models based on deep learning have made remarkable progress, and some of these
methodologies utilizing character-level features have successfully avoided such
a difficult problem. However, when a model is fed character-level features of
the above languages, it often causes overfitting due to a large number of
character types. In this paper, we propose a CE-CLCNN, character-level
convolutional neural networks using a character encoder to tackle these
problems. The proposed CE-CLCNN is an end-to-end learning model and has an
image-based character encoder, i.e. the CE-CLCNN handles each character in the
target document as an image. Through various experiments, we found and
confirmed that our CE-CLCNN captured closely embedded features for visually and
semantically similar characters and achieves state-of-the-art results on
several open document classification tasks. In this paper we report the
performance of our CE-CLCNN with the Wikipedia title estimation task and
analyse the internal behaviour.
