A Combination of RNN and CNN for Attention-based Relation Classification论文阅读笔记
摘要 关系分类在自然语言处理（NLP）领域中起着重要作用。先前有关关系分类的研究已经证明了使用卷积神经网络（CNN）和递归神经网络（RNN）的有效性。在本文中，我们提出了一个结合RNN和CNN的模型（RCNN），这将充分发挥它们各自的优势：RNN可以学习时间和上下文特征，尤其是两个实体之间的长期依赖性，而CNN可以捕捉更多潜在功能。我们在SemEval-2010 Task 8数据集1上进行了实验，
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相关文章
- 1. 论文笔记-recurrent CNN for text classification
- 2. 论文笔记--Appearance-and-Relation Networks for Video Classification
- 3. 论文阅读笔记《Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification》
- 4. Reinforcement Learning for Relation Classification from Noisy Data阅读笔记
- 5. 阅读笔记Surflet-Pair-Relation Histograms: A Statistical 3D-Shape Representation for Rapid Classification
- 6. 《Designing and Training of A Dual CNN for Image Denoising》阅读笔记
- 7. 论文阅读笔记（三十三）：Relation Network for Object Detection
- 8. CNN for Sentence Classification-textcnn阅读笔记
- 9. 【论文阅读笔记】Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
- 10. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》论文阅读笔记