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.
Another method is to learn glyph features cause Chinese characters are composed of graphical components, so that character glyph features can be learned directly from the bitmaps of characters by convolutional auto-encoder REF .
Learning Chinese Word Representations From Glyphs Of Characters
ArXiv
ArXiv
Computer Science