  Word embeddings have been widely adopted across several NLP applications.
Most existing word embedding methods utilize sequential context of a word to
learn its embedding. While there have been some attempts at utilizing syntactic
context of a word, such methods result in an explosion of the vocabulary size.
In this paper, we overcome this problem by proposing SynGCN, a flexible Graph
Convolution based method for learning word embeddings. SynGCN utilizes the
dependency context of a word without increasing the vocabulary size. Word
embeddings learned by SynGCN outperform existing methods on various intrinsic
and extrinsic tasks and provide an advantage when used with ELMo. We also
propose SemGCN, an effective framework for incorporating diverse semantic
knowledge for further enhancing learned word representations. We make the
source code of both models available to encourage reproducible research.
