I am trying to build text classifier, Usually, we have one text column and ground truth. But I am working on a problem where dataset contains many text features. I am exploring different ways how to utilize different text features.
For example, my dataset looks like this
Index_no domain comment_by comment research_paper books_name 01 Science Professor Thesis needs Evolution of MOIRCS more work Quiescent Deep Galaxies as a Survey Function of Stellar Mass 02 Math Professor Doesn't follow Evolution of Latex format Quiescent nonlinear Galaxies as a dispersive Function of equations Stellar Mass
This is just a dummy dataset, Here my ground truth (Y) is domain and features are
If I am using any NLP model (RNN-LSTM, Transformers etc), those models usually take one 3 dim vectors, for that if I am using one text column that works but How to many text features for text classifier?
What I've tried :
1) Joining all column and making a long string
Professor Thesis needs more work Evolution of Quiescent Galaxies as a Function of Stellar Mass MOIRCS Deep Survey
2) Using a token between columns
<CB> Professor <C> Thesis needs more work <R> Evolution of Quiescent Galaxies as a Function of Stellar Mass <B> MOIRCS Deep Survey
<CB> comment_by ,
Should I use
<CB> at the beginning or use like this?
Professor <1> Thesis needs more work <2> Evolution of Quiescent Galaxies as a Function of Stellar Mass <3> MOIRCS Deep Survey
3) Using different dense layers (or embedding) for each column and concatenate them.
I've tried all three approaches, Is there any other approach I can try to improve the model accuracy? or extract, combine, join the better features?
Thanks in advance!