从文本中删除停用词是否会影响斯坦福核心nlp NER的性能? [英] Does removing stop words from text affect stanford core nlp NER performance?

问题描述

我们正在尝试对数百万条评论/反馈实施名称实体识别，并且过程似乎很慢.我们正在考虑从文本中删除停用词/常见词，并在其上应用ner.删除停用词会影响ner的准确性吗?

we are trying to implement name entity recognition on millions of comments/feedback and the process appears to be slow. We are thinking of removing stop words/frequent words from the texts and apply ner on them. Does removing stop words affect the accuracy of ner?

推荐答案

我认为，如果您在删除停用词的句子上运行，您将获得可观的F1分数.最终，您将不得不对其进行试验，看看质量是否可以满足您的需求.

I think it's plausible you will get respectable F1 scores if you run on a sentence with the stop words removed. Ultimately you will have to experiment with it and see if the quality is acceptable for your needs.

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