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NLP之WE之CBOW&Skip-Gram：CBOW&Skip-Gram算法相关论文、原理配图、关键步骤详细攻略
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》
| 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. |
CBOW&Skip-Gram算法原理配图
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
