I received a data set containing a string of text and a label that categorizes that text into one of 50 categories. I'm hoping to build a model that predicts which category a string of text belongs in.
When the dataset was put together, it was assembled under the assumption that each string of text can only belong to one group. In actuality, the text can belong to more than one group simultaneously.
Instead of going back to the drawing board and manually labeling the data again, I want to try and convert this single-label data set into a multi-label data set.
I've tried one method with questionable results. I built a linear regression that predicts each category individually, and appended those predictions to the original data. While this gave me data in the structure I needed, it yielded lackluster results. Most strings of text still only belong to one category (many should belong to multiple), and a good portion weren't assigned any label at all.
It seems that even if I can "Frankenstein" this data together, it may not serve as quality training data. I'm curious, is there's any great way of transforming this single-label data into multi-label data?