《Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning》
問題：早前對NER的大多數研究都集中在一組預定義的NER類型上，比如人名、地名、組織名、日期等等，提供了一定數量的標記數據來訓練模型。然而，不同的應用場景需要特定的實體類型，如電子商務領域的「Brand」和「Product」，金融行業的「Company」。考慮到人工標註的高成本，爲每種新的NER類型標註大量數據是不可行的，但有時可以使用小規模數據。 方法：遠程監控可以爲新型NER自動生成大規模的標
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相關文章
- 1. Reinforcement learning and Deep learning
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- 8. Playing Atari with Deep Reinforcement Learning
- 9. Partial Transfer Learning with Selective Adversarial Networks
- 10. Self-Supervised Representation Learning