Suppose that my task is to label news articles; that is, to classify which category a news article belongs to. Using the labelled data (with old labels) that I have, I have trained a model for this.
For relevancy purposes, certain labels may be split into multiple new labels. For example, 'Sports' may split into 'Sports' and 'E-Sports'. Because of these new labels, I will need to retrain my model. However, my training data is labelled with the old labels. What can I do to address these 'label updates'?
My idea: Perhaps use some unsupervised clustering method (K-means?) to split the data with the old labels into the new labels. (But how can we be certain that which cluster has what new label?) Then use this 'updated' data to train a model. Is this correct?