Comments (4)
您理解的没错,无论训练还是测试,都只使用了实体的最后一个词。这篇文章的重点是关注实体重叠的问题,因此对多个词的实体进行了简化。真实情况中很多实体确实是包含多个词的,这也是这篇文章暂时不能解决的问题。
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感谢您的回复。那这么说来,那些针对实体多词问题来解决的模型,直接引用您论文的结果进行比较应当是不合理的,我就是因为看到近期的论文都直接引用您论文的58.7(F1值)进行比较,所以产生了这样的疑惑。
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是的,我也关注到了这个问题,直接对比不是很合理。但是,如果用抽取完整实体的p,r,f来对比我论文中的p,r,f,并且还超过了的话,确实能说明那个方法比我的方法更好。因为抽取完整实体的难度显然是不会低于只抽取一个词的难度。当然,论文中最好还是能有个脚注来说明评价标准的差异。
更进一步来说,如果考虑完整的实体了,其实数据集的统计情况也是会发生改变的。只考虑最后一个字时,两个实体相同,但是考虑完整的实体时,两个实体就未必相同了。比如“michael jordan”和“air jordan”在只考虑最后一个字时被认为是相同的,但是考虑完整实体时是不同的。从这个角度来说,由于实际上数据集不同了,就不应该跟我这篇文章进行对比了,而是重新给出完整实体情况下的统计结果和实验结果。
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感谢您的解答。谢谢!
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Related Issues (18)
- About the dependencies HOT 1
- 数据集处理问题 HOT 1
- WebNLG原始数据 HOT 1
- 如何处理数据 HOT 4
- 一些疑惑 HOT 2
- What is the accuracy, precision report in paper? HOT 2
- f1/precision/recall HOT 2
- webnlg数据集问题 HOT 1
- data get
- Where is the embedding for relations? HOT 4
- dataset problem HOT 1
- f1/precision/recall HOT 1
- How to recover triples from ids for webnlg?
- NYT原始数据集 HOT 5
- 关于实体抽取部分 HOT 1
- entity包含多个词的情况 HOT 1
- relation facts order question? HOT 2
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