I work in healthcare and am trying to see if I can use NLP for a classification task on complex sentences. To explain, I have different labels, and each label has multiple levels. I am not sure on the correct terminology however. I have label X, and X always exists as one of 4 'levels':
- 'absent' or 0
- 'few', or 1
- 'many', or 2
- 'everywhere', or 3
Example sentences then look like: 'I have no X', or 'I have a little bit of X', or 'there is X everywhere'. However, I also have labels Y and Z, which also have multiple levels. To complicate things further, one sentence can often contain information about multiple labels. As an example:
- 'X and Y are both absent in this man'. classification would be X:0, and Y:0
- 'He has no X but a lot of Z'. classification would be X:0 , and Z:2
- 'There is Z everywhere, but very little Y'. classification would be Z:3 and Y:1
Sentences in my corpus can also be about something completely different in which I am not interested:
- 'Q was quite small'.
Does anyone know the correct terminology for a problem like this? I have tried doing research on similar problems, I was thinking a regression based solution might be needed for the different levels, although pure multiclass classification might also work. I have quite a lot of experience with multilabel and multiclass classification problems, but without the multiple ordered 'levels' in the data I show here. Also, if anyone has any suggestions for approaches, that would also be very helpful! I do have a few thousand high quality training sentences.