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.

本文分析词嵌入训练的三个关键组成部分，并将现有的基于神经网络的词嵌入算法系统化，并在相同语料库下进行比较评估。作者提供了几条简单指南以训练词嵌入。作者发现，选择合适领域的语料库比语料库大小更为重要。在此基础上使用更大的语料库能得到更好的结果。本文还展示了更快的模型在大多数情况下提供足够的性能，而更复杂的模型可以在培训语料库足够大的情况下使用，并且，对于迭代停止指标，应该依赖于所需任务的开发集，而不是训练嵌入的验证损失。

如何生成良好的词向量？