【论文笔记】Relation Classification via Multi-Level Attention CNNs
一、概要 该paper发于ACL2016上，主要提出了一个基于多Attention机制CNN网络的实体关系抽取方法，其中Attention机制主要是：Input Attention Mechanism和Convolutional Max-pooling Attention Mechanism。在不依赖于外部先验知识和特征的情况下，就已经能够得到高于当前最好方法的结果。 二、模型方法 2.1 模
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相关文章
- 1. Relation Classification via Multi-Level Attention CNNs 论文研读和模型调试
- 2. Relation Classification via Convolutional Deep Neural Network 论文笔记
- 3. 论文笔记：Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
- 4. 论文阅读笔记《Few-shot Classification via Adaptive Attention》
- 5. 【论文笔记】Neural Relation Extraction with Multi-lingual Attention
- 6. 译文：Relation Classification via Multi-Level Attention CNNs 使用多层级attention机制的CNN进行关系分类
- 7. 论文笔记：Residual Attention Network for Image Classification
- 8. Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction 论文笔记
- 9. A Combination of RNN and CNN for Attention-based Relation Classification论文阅读笔记
- 10. 【论文研读】Relation Classification via Convolutional Deep Neural Network