Comments (7)
- 不是的,实体有自己的类别,默认一个实体的所有mention都是这个类别。区别:比如mention type对应“时间”,而role type是“冻结开始时间”
- 没看懂
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1 我用一个英文数据集里面 最后把这些mention的类型打印出来看都是一样的 只有 0 1
但是原始的类型肯定不是这样
2. 举个例子:文本里面 标记的entity 一共 10个 但是事件却只有其中5个 ,或者更直接点事件里面一个都没用到
这样训练应该是可以的 但是从感觉上来说 这个多余的实体是否有必须要加入 模型里面
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- 根据您的描述,应该是数据集处理的时候有些问题。因为是您自己的数据集,所以没办法定位到具体的问题;
- 都行,实验结果证明加上之后可以提升NER部分的P值。
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我看有些数据里面有othertype 这个是不是就是我说的多余实体是吧 ~~~
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是的,但不是“多余实体”的全部。可以对数据集做个简单分析,就可以看出哪些是“多余实体”了。
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多谢~~
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我现在发现在英文的数据集上面的ner recall 很低哈 这个我已经将数据的下标改成piece 后的下标 还是这样
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Related Issues (20)
- 实验结果 HOT 4
- 相似度的一些问题 HOT 8
- 分布式训练 HOT 3
- importance分数 HOT 15
- deppn模型F1只有33 HOT 2
- "pred_results"中的classification得分 HOT 25
- 分句 (uncommon sentence cutoff in DuEE-fin) HOT 14
- Duee_Fin预测结果 HOT 2
- 测试集结果 HOT 2
- 单事件&多事件 HOT 3
- Greedy-Dec模型如何运行? HOT 6
- Evaluation Metric HOT 11
- similarity calculation HOT 1
- pretrained model weight HOT 1
- 多事件 HOT 1
- 使用o2m格式的数据时,需要修改那些代码呢 HOT 1
- Potential performance issue: plotting slow in matplotlib == 3.3.0 HOT 1
- 请问老师怎么在自己的数据集上进行训练呢? HOT 14
- 关于ptpcg论文的一些问题 HOT 4
- 论文中的一个问题 HOT 3
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