  In this paper, we propose new methods to learn Chinese word representations.
Chinese characters are composed of graphical components, which carry rich
semantics. It is common for a Chinese learner to comprehend the meaning of a
word from these graphical components. As a result, we propose models that
enhance word representations by character glyphs. The character glyph features
are directly learned from the bitmaps of characters by convolutional
auto-encoder(convAE), and the glyph features improve Chinese word
representations which are already enhanced by character embeddings. Another
contribution in this paper is that we created several evaluation datasets in
traditional Chinese and made them public.
