compare node2vec, gcn, gat on word context graph for word embeddings.
尝试使用GCN做词向量。没有node2vec效果好，但是比随机的好。然后又做了GAT，比GCN拟合慢，而且容易进入局部最优。
嵌入方法:
- 完全随机的参数。（GCN)
- 监督预测结巴词性。(GCN,GAT)
- 监督预测（skip-gram）词上下文。(GCN,GAT)
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try different opts on word context graph with GCN and GAT to obtain word embeddings.
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