NLP之WE之CBOW&Skip-Gram：CBOW&Skip-Gram算法概念相关论文、原理配图、关键步骤之详细攻略
目录
CBOW&Skip-Gram算法相关论文
CBOW 模型和Skip-Gram 模型，参考论文《Efficient Estimation of Word Representations in Vector Space》
论文地址：https://arxiv.org/pdf/1301.3781.pdf
| We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. | 我们提出了两种新颖的模型体系结构，用于从非常大的数据集中计算单词的连续矢量表示。 在单词相似性任务中测量这些表示的质量，并将结果与基于不同类型的神经网络的性能最佳的以前的技术进行比较。 我们观察到准确性的大幅提高，而计算成本却低得多，即从16亿个单词的数据集中学习高质量的单词向量只需不到一天的时间。 此外，我们证明了这些向量在我们的测试集上提供了最新的性能，用于测量句法和语义词的相似性。 |
CBOW&Skip-Gram算法原理配图对比
1、CBOW模型之用一个单词预测一个单词
2、CBOW模型之用多个单词预测一个单词
3、选取噪声词进行分类的CBOW模型
