3 ms·

Thanks for the reply! How many labeled texts would you recommend for class? There are more than 100 third level classes, so i was trying to avoid it but if it

• PaulHoule 5y ago
  Well, if you have a large number of categories you have a difficult problem. One trouble is that as the number of classes expands the more likely your categorization is bad from an ontological perspective. For instance, something in class #34 might also be in class #79, you might have a hard time telling if something is in #55 or #86, etc. Even if your categorization can be implemented perfectly you will still have the problem of class imbalance. If we number classes by prevalence it might be that class #1 occurs 20% of the time, and class #99 occurs at a 1-in-a-million rate and you might not be able to find one at all in your data set. If you make a set of labeled classes that is representative of how often they occur you are going to have too many instances of the most common classes and not enough of the rare classes. In principle ML algorithms learn not just the characteristics of the classes but also learn the frequency. Pretty obviously you could enrich the labeled data with more examples of rare cases and difficult cases, but the algorithm will then get the wrong idea about the prevalence. I wish I could say I knew some way to tell the system that the "prior distribution" of classes is different from what's seen in the training examples, but I don't. Your best bet is to start with a simplified problem: target either the top of hierarchy or the most common examples. You've got to get into some place where imbalances are not outrageous.
  • jesuswr 5y ago
    Ok, thanks! I'll focus on the top level first