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