How to Generate a Good Word Embedding?

The authors analyze three critical components in training word embeddings: model, corpus, and training parameters. They systematize existing neural-network-based word embedding methods and experimentally compare them using the same corpus. They then evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for supervised tasks, and using it to initialize neural networks. They also provide several simple guidelines for training good word embeddings.