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【cs224n-15】Constituency Parsing and Tree Recursive Neural Networks
時間 2021-01-02
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CS224N
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本文討論一種新的類型的模型:遞歸神經網絡(RNNs),區別於循環神經網絡(RNN)的是,前者是後者的一個超集。 遞歸神經網絡 遞歸神經網絡(左圖):需要樹形結構 循環神經網絡(右圖):不能捕捉沒有前綴背景的短語,最後的向量經常捕捉最後的詞的太多信息 遞歸神經網絡非常適合具有嵌套層次結構和內在遞歸結構的設置。思考一個句子 「A small crowd quiet
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相關文章
1.
Lecture 18: Tree Recursive Neural Networks, Constituency Parsing, and Sentiment
2.
用 Recursive Neural Networks 得到分析樹
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
>>更多相關文章<<