PROBABILITY ESTIMATION FROM TEXT EVIDENCE & OCCURRENCE

Hi All ,
Thanks for making such a brilliant Probability estimation library , i have some doubts for a project , can you all tell me how to achieve that using Probability estimation library or any of your methods ..

PROBLEM STATEMENT - i am trying to create a probability tool for creating scores of importance eg (9/10, 8/10 or 6/10 ) depending on the degree of importance ,from a book text corpus , i am trying to calculate the probability of certain topic coming in the exam using previous year question papers . i have around 10 yrs of question papers comprising of 300 question in total from the same syllabus . but books are different . since the questions are not straight forward , i am unable to do this

WHAT HAVE I TRIED - i tried using topic modelling and LDA method in it ,but it gives only important topics or words from a text corpus . i was able to generate important topics from text , using GENSIM library but it couldn't solve my problem of matching and comparing it with the questions from the previous years question papers as some times questions were indirect or twisted .

WHAT I AM HOPING - i am hoping is there a way Probability estimation library can help me with understanding the indirect questions or twisted questions to generate a topic which may help in matching it with the topics in book corpus from the same syllabus thus generating a probability of topics occuring in the exam using previous years question papers .
Looking forward for your help , any kind of help in solving this particular problem statement would be of great help to us

Thanks

The issue names no repository file, test, or entry point. Start by clarifying whether a TensorFlow Probability feature is requested and how indirect questions, topics, and scores should be defined; done would require a concrete, reproducible change or documentation request.