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作者你好,我今天在网上看了一篇text cnn的文章,和项目中的思路有较大的区别,不清楚你这边有没有做过相关的对比 https://towardsdatascience.com/nlp-learning-series-part-3-attention-cnn-and-what-not-for-text-classification-4313930ed566 文中对于卷积核的使用思路是,卷积同时覆盖多个(1,2,3,5个)词,使用卷积提取词之间的特征,最后用于分类,项目中的卷积结构更多的是提取flatten后词向量的局部特征,从直觉上感觉太过于局限在word(character) embedding部分,是否有尝试过这两种方式之间的差异?
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作者你好,我今天在网上看了一篇text cnn的文章,和项目中的思路有较大的区别,不清楚你这边有没有做过相关的对比
https://towardsdatascience.com/nlp-learning-series-part-3-attention-cnn-and-what-not-for-text-classification-4313930ed566
文中对于卷积核的使用思路是,卷积同时覆盖多个(1,2,3,5个)词,使用卷积提取词之间的特征,最后用于分类,项目中的卷积结构更多的是提取flatten后词向量的局部特征,从直觉上感觉太过于局限在word(character) embedding部分,是否有尝试过这两种方式之间的差异?
The text was updated successfully, but these errors were encountered: