  We analyze three critical components of word embedding training: the model,
the corpus, and the training parameters. We systematize existing
neural-network-based word embedding algorithms and compare them using the same
corpus. We 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. We also provide several simple guidelines for
training word embeddings. First, we discover that corpus domain is more
important than corpus size. We recommend choosing a corpus in a suitable domain
for the desired task, after that, using a larger corpus yields better results.
Second, we find that faster models provide sufficient performance in most
cases, and more complex models can be used if the training corpus is
sufficiently large. Third, the early stopping metric for iterating should rely
on the development set of the desired task rather than the validation loss of
training embedding.
