Disconnected Recurrent Neural Networks for Text Categorization
Disconnected Recurrent Neural Networks（DRNN） CNN模型擅長抽取位置不變的局部特徵，但是對於一些長距離依賴的問題處理的並不好。 RNN模型則更適合處理長距離依賴問題。 DRNN將RNN和CNN的優點結合起來，利用RNN增加了文本的長期依賴性，利用CNN增強了對局部的檢測和位置不變性。該模型的結構是將k個RNN作爲CNN中的卷積核，其中k爲window s
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相關文章
- 1. Recurrent Convolutional Neural Networks for Text Classification
- 2. Taming Recurrent Neural Networks for Better Summarization
- 3. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
- 4. DPCNN做文本分類《Deep Pyramid Convolutional Neural Networks for Text Categorization》
- 5. 019 Recurrent Neural Networks
- 6. RNN(Recurrent Neural Networks)
- 7. Recurrent Neural Networks
- 8. Recurrent Neural Networks 簡述
- 9. 論文《Recurrent Convolutional Neural Networks for Text Classification》總結
- 10. Recurrent Convolutional Neural Networks for Text Classification閱讀筆記