What are the simplest methods for the label noise problem? If I have enough low quality data from unsupervised methods or rule-based methods.

I read from https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise ,but these methods are a little complex for me.

In detail, I deal with a multi-label classification task. First I crawl web page such as wiki and use regex-based rule to mark the label. The model input is the wiki title and the model output is the rule-matched labels from wiki content. My task is to predict the labels for the wiki title.

Do you think removing the wrong data predicted by trained model is a simple but effective method?
If the labeling people is sufficient,
the best solution is to re-label the noisy data.