論文閱讀筆記：Transformation Networks for Target-Oriented Sentiment Classification
原文鏈接 本文是香港中文大學和騰訊AI實驗室2018年在ACL上發表的論文 摘要 本文模型爲三層結構，最下面一層爲雙向LSTM，最上一層爲CNN，替代基於注意機制的RNN 去提取最重要的分類特徵，由於 CNN 很難捕捉目標實體信息，所以本文提出了一個特徵變換組件來將實體信息引入到單詞的語義表示當中。但這個特徵變換過程可能會使上下文信息丟失。所以本文又提出了一種「上下文保留」機制，可將帶有上下文
相關文章
- 1. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記
- 2. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 3. Attention-based LSTM for Aspect-level Sentiment Classification論文閱讀筆記
- 4. 【論文閱讀筆記】Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment
- 5. 【論文閱讀筆記】Convolutional Neural Networks for Sentence Classification
- 6. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 7. Convolutional Neural Networks for Sentence Classification 閱讀筆記
- 8. Graph Convolutional Networks for Text Classification閱讀筆記
- 9. 【論文閱讀筆記】MULTI-SCALE DENSE NETWORKS FOR RESOURCE EFFICIENT IMAGE CLASSIFICATION
- 10. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記
- 更多相關文章...
- • RSS 閱讀器 - RSS 教程
- • PHP 實例 - AJAX RSS 閱讀器 - PHP教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • JDK13 GA發佈：5大特性解讀
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification 論文閱讀筆記
- 2. 【論文閱讀筆記】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 3. Attention-based LSTM for Aspect-level Sentiment Classification論文閱讀筆記
- 4. 【論文閱讀筆記】Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment
- 5. 【論文閱讀筆記】Convolutional Neural Networks for Sentence Classification
- 6. 論文閱讀：《Convolutional Neural Networks for Sentence Classification》
- 7. Convolutional Neural Networks for Sentence Classification 閱讀筆記
- 8. Graph Convolutional Networks for Text Classification閱讀筆記
- 9. 【論文閱讀筆記】MULTI-SCALE DENSE NETWORKS FOR RESOURCE EFFICIENT IMAGE CLASSIFICATION
- 10. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記