Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
概述： 現在的機器學習算法的核心都是利用有關領域的有標籤或者無標籤的數據集，雖然已經存在遷移學習可以將一個領域的知識用於其他領域，但是這些方法還是擴展性比較差，並且只能應用於特定領域。所以能不能有一種訓練方式，除了基於訓練數據的學習之外，能夠爲模型注入一般的世界知識的方式進行訓練？該論文主要是用世界知識（知識圖譜三元組的形式）來增強模型。目標就是建立一種深度學習模型，該模型可以從世界知識中中提取出
相關文章
- 1. Natural Language Processing[論文合集]
- 2. CS224d: Deep Learning for Natural Language Process
- 3. Memory Augmented Graph Neural Networks for Sequential Recommendation翻譯
- 4. Attention and Augmented Recurrent Neural Networks
- 5. 文獻閱讀：Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Under
- 6. Knowledge Graph Convolutional Networks for Recommender Systems
- 7. EMNLP2019 | Knowledge-Aware Graph Networks
- 8. Graph Neural Networks for Social Recommendation
- 9. Few-Shot Learning with Graph Neural Networks
- 10. 【筆記】CS224N Natural Language Processing with Deep Learning
- 更多相關文章...
- • RSS
元素 - RSS 教程 - • Swift for 循環 - Swift 教程
- • Java Agent入門實戰（三）-JVM Attach原理與使用
- • YAML 入門教程
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。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Natural Language Processing[論文合集]
- 2. CS224d: Deep Learning for Natural Language Process
- 3. Memory Augmented Graph Neural Networks for Sequential Recommendation翻譯
- 4. Attention and Augmented Recurrent Neural Networks
- 5. 文獻閱讀：Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Under
- 6. Knowledge Graph Convolutional Networks for Recommender Systems
- 7. EMNLP2019 | Knowledge-Aware Graph Networks
- 8. Graph Neural Networks for Social Recommendation
- 9. Few-Shot Learning with Graph Neural Networks
- 10. 【筆記】CS224N Natural Language Processing with Deep Learning