📄 中文摘要
Word2Vec 通过负采样技术显著加快了训练速度。负采样的原理是随机选择一部分不需要预测的单词，从而优化模型的训练过程。例如，在预测单词“Antelope”时，仅在输入位置上将其标记为1，而其他单词则标记为0。这种方法减少了计算量，使得模型能够更高效地学习词汇之间的关系，提升了训练的速度和效果。
理解 Word2Vec – 第七部分：负采样如何加速 Word2Vec
📄 English Summary
Understanding Word2Vec – Part 7: How Negative Sampling Speeds Up Word2Vec
Word2Vec significantly speeds up training through a technique called negative sampling. Negative sampling works by randomly selecting a subset of words that are not to be predicted during the optimization process. For instance, when predicting the word 'Antelope', only 'Antelope' is marked as 1 in the input position, while all other words are marked as 0. This approach reduces computational load, allowing the model to learn the relationships between words more efficiently, thereby enhancing both the speed and effectiveness of training.