Comments (5)
- 您好,我们在这篇工作中仅通过case study的方式展示我们的工作在WSD的潜在应用(详情可以参考paper的4.5.1节),目前没有严格测试是否可以直接应用在WSD上。
- 这是一个非常好的问题,我们的模型将会同时训练义原和词的表示,它们有可能能为义原预测的有关工作提供有益的输入,您可以尝试利用我们在https://github.com/thunlp/SE-WRL-SAT 开源的词向量和义原向量进行有关的尝试,我们欢迎您在此与我们交流您的观察与发现!
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非常感谢您的回答。我目前正在复现您的代码,发现,每运行一次,结果都不一样。
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我们的代码是预先固定随机种子的,应该跑多少次重复实验都有相同的结果,您是使用自己复现的代码还是使用我们的代码?如果使用自己复现的代码,请检查您是否固定了随机种子--模型训练过程中需要进行负采样,如果不固定随机数种子,那么就会多次跑出来的结果都不太一样
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我用的是这个链接里的代码 https://github.com/thunlp/SE-WRL-SAT
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| WordSim-240 Score | WordSim-297 Score
重新第一次 | 0.6216854959448419 | 0.6278795368801042
重新第二次 | 0.6217434132985868 | 0.635456621032264
重新第三次 | 0.6238719078684416 | 0.6280517586441791
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Related Issues (20)
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- 您好,我目前已经跑出了SAT的结果,在similarity和analogy的mean rank指标上表现都很好,但唯独在analogy的accuracy指标上与论文中的结果相差很远。我的参数设置如下:
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