sileod / discsense Goto Github PK

Hi :) Thanks for the great work. DiscSense deserves more recognition.
It reveals so much more potential for discourse analysis, especially pertaining to its role in semantics.

As a peer researcher in the similar field, a great use case of DiscSense is the understanding of text semantics through simple token matching.
If the semantic labels presented in DiscSense were meaningful enough, such a semantic analysis system would be possible even without sophisticated BERT-like encoders involved.

However, you mention Confidence (Prior) calculations.
I read your Paper but it is difficult to conceptually grasp what you mean by "Confidence" and "Prior".

How exactly are these values computed (I find it unclear in your LREC paper)? And what do you qualitatively mean by "Confidence" and "Prior"?
I hope I could receive some help here.