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