  Recent studies have consistently given positive hints that morphology is
helpful in enriching word embeddings. In this paper, we argue that Chinese word
embeddings can be substantially enriched by the morphological information
hidden in characters which is reflected not only in strokes order sequentially,
but also in character glyphs spatially. Then, we propose a novel Dual-channel
Word Embedding (DWE) model to realize the joint learning of sequential and
spatial information of characters. Through the evaluation on both word
similarity and word analogy tasks, our model shows its rationality and
superiority in modelling the morphology of Chinese.
