Comments (4)
我好像找到了。就是第一阶段是不管aspect-opinion对的事情,把所有的aspect和opinion组合后,丢给第二阶段去做。并且在第二阶段中,实际训练过程中,无效的aspect-opinion对的target为[0]len(类别情感种类);有效对的target则为[0]len(类别情感种类-1)+[1],当然这个1的位置是视目标位置而定的,我这么写只是为了区分。
如果我的想法有错误的话,还麻烦指正。期待你的回复!
from acos.
上述的×号丢失了。
我好像找到了。就是第一阶段是不管aspect-opinion对的事情,把所有的aspect和opinion组合后,丢给第二阶段去做。并且在第二阶段中,实际训练过程中,无效的aspect-opinion对的target为[0]×len(category×情感种类);有效对的target则为[0]×len(category×情感种类-1)+[1],当然这个1的位置是视目标位置而定的,我这么写只是为了区分。
如果我的想法有错误的话,还麻烦指正。期待你的回复!
from acos.
是这样的。一阶段得到aspect集合和opinion集合,对两个集合做笛卡尔积处理:
ACOS/Extract-Classify-ACOS/tokenized_data/get_1st_pairs.py
Lines 40 to 44 in d522a0b
二阶段判断aspect和opinion是否构成合法配对(全0为无效配对),并为每个合法的匹配做category-sentiment的分类(对应位置为1)。
from acos.
是这样的。一阶段得到aspect集合和opinion集合,对两个集合做笛卡尔积处理:
ACOS/Extract-Classify-ACOS/tokenized_data/get_1st_pairs.py
Lines 40 to 44 in d522a0b
二阶段判断aspect和opinion是否构成合法配对(全0为无效配对),并为每个合法的匹配做category-sentiment的分类(对应位置为1)。
好的,非常感谢!
from acos.
Related Issues (17)
- issues for step1 eval_metrics.py HOT 3
- 脚本文件运行错误 HOT 4
- 关于超参数 HOT 3
- How to prepare inference script from model output HOT 5
- annotation tools HOT 3
- 现有方法是不是已经被超过了? HOT 1
- 对于子集的疑问 HOT 2
- 如何使用训练好的模型进行预测 HOT 5
- 其他3种基线的source code
- environment configuration HOT 2
- Issues for run_step2.py HOT 6
- Issues for get_1st_pairs.py HOT 2
- 数据集aspect,opinion, AO pair规模问题 HOT 1
- pickle.UnpicklingError HOT 1
- 关于数据集的疑问 HOT 2
- 关于step2编码问题 HOT 4
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from acos.