《Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning》
问题：早前对NER的大多数研究都集中在一组预定义的NER类型上，比如人名、地名、组织名、日期等等，提供了一定数量的标记数据来训练模型。然而，不同的应用场景需要特定的实体类型，如电子商务领域的“Brand”和“Product”，金融行业的“Company”。考虑到人工标注的高成本，为每种新的NER类型标注大量数据是不可行的，但有时可以使用小规模数据。 方法：远程监控可以为新型NER自动生成大规模的标
相关文章
- 1. Reinforcement learning and Deep learning
- 2. Supervised learning and Unsupervised learning
- 3. Deep Reinforcement Learning
- 4. Reinforcement learning: integrating learning and planning, exploitation and exploration
- 5. Reinforcement Learning, Fast and Slow
- 6. FixMatch Simplifying Semi-Supervised Learning with Consistency and Confidence
- 7. 【论文读后感】《Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised》
- 8. Playing Atari with Deep Reinforcement Learning
- 9. Partial Transfer Learning with Selective Adversarial Networks
- 10. Self-Supervised Representation Learning
- 更多相关文章...
- • XML Schema annotation 元素 - XML Schema 教程
- • XSL-FO table-and-caption 对象 - XSL-FO 教程
- • RxJava操作符（七）Conditional and Boolean
- • Java Agent入门实战（一）-Instrumentation介绍与使用
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 and Deep learning
- 2. Supervised learning and Unsupervised learning
- 3. Deep Reinforcement Learning
- 4. Reinforcement learning: integrating learning and planning, exploitation and exploration
- 5. Reinforcement Learning, Fast and Slow
- 6. FixMatch Simplifying Semi-Supervised Learning with Consistency and Confidence
- 7. 【论文读后感】《Self-Attention Enhanced CNNs and Collaborative Curriculum Learning for Distantly Supervised》
- 8. Playing Atari with Deep Reinforcement Learning
- 9. Partial Transfer Learning with Selective Adversarial Networks
- 10. Self-Supervised Representation Learning