【cs224n-15】Constituency Parsing and Tree Recursive Neural Networks

时间 2021-01-02

标签 CS224N 繁體版

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本文讨论一种新的类型的模型：递归神经网络（RNNs）,区别于循环神经网络（RNN）的是，前者是后者的一个超集。递归神经网络递归神经网络（左图）：需要树形结构循环神经网络（右图）：不能捕捉没有前缀背景的短语，最后的向量经常捕捉最后的词的太多信息递归神经网络非常适合具有嵌套层次结构和内在递归结构的设置。思考一个句子 “A small crowd quiet

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1. Lecture 18: Tree Recursive Neural Networks, Constituency Parsing, and Sentiment
2. 用 Recursive Neural Networks 得到分析树
3. Ranking with Recursive Neural Networks and Its Application to Multi-document Summarization
4. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
5. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
6. Neural Networks and Deep Learning
7. Attention and Augmented Recurrent Neural Networks
8. Graph-based Dependency Parsing with Graph Neural Networks
9. [ICLR19] ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS
10. WITH RECURSIVE and MySQL
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1. Lecture 18: Tree Recursive Neural Networks, Constituency Parsing, and Sentiment
2. 用 Recursive Neural Networks 得到分析树
3. Ranking with Recursive Neural Networks and Its Application to Multi-document Summarization
4. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
5. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
6. Neural Networks and Deep Learning
7. Attention and Augmented Recurrent Neural Networks
8. Graph-based Dependency Parsing with Graph Neural Networks
9. [ICLR19] ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS
10. WITH RECURSIVE and MySQL

>>更多相关文章<<