Reinforcement Learning for Relation Classification from Noisy Data
主要贡献 提出一个新的关系分类模型，它有实体选择器与关系分类器构成。它能够在句子级别提取关系。 将实体选择问题转换成强化学习问题，这使得不需要实体选择的标签，而只需要关系分类器的弱监督的回馈就能进行实体选择。 摘要 现在的关系分类方法都是依赖拍距离监督假设（distance supervision assume）的，它假设一系列提到一对实体的句子，都是在描述这对实体的一种关系。类似于这种思想的方法
相关文章
- 1. Reinforcement Learning for Relation Classification from Noisy Data阅读笔记
- 2. Learning from Uncertainty for Big Data
- 3. Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification （2019 AAAI）
- 4. 论文笔记：Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
- 5. Learning from Noisy Anchors的思考
- 6. Noise2Void - Learning Denoising from Single Noisy Images
- 7. Open Relation Extraction- Relational Knowledge Transfer from Supervised Data to Unsupervised Data
- 8. (转) Deep Reinforcement Learning: Pong from Pixels
- 9. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 10. 《Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning》
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • Java Agent入门实战（三）-JVM Attach原理与使用
- • JDK13 GA发布：5大特性解读
相关标签/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。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Reinforcement Learning for Relation Classification from Noisy Data阅读笔记
- 2. Learning from Uncertainty for Big Data
- 3. Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification （2019 AAAI）
- 4. 论文笔记：Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
- 5. Learning from Noisy Anchors的思考
- 6. Noise2Void - Learning Denoising from Single Noisy Images
- 7. Open Relation Extraction- Relational Knowledge Transfer from Supervised Data to Unsupervised Data
- 8. (转) Deep Reinforcement Learning: Pong from Pixels
- 9. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 10. 《Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning》