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Virendra Sawant (VirendraSawant)
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VirendraSawant Member since 2017 3 posts
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Posted: December 3, 2020
Last activity: December 3, 2020

Topic Detection Model in NLP not training via Feedback - FScore decreasing

We are using NLP Topic detection (Max Entropy Model) for categorization of emails. We have also included feedback loop to correct the right category in case the category detected was wrong, We are using pxCaptureTAFeedback for feedback and pxUpdateModels to update model. We are removing the salutations and signature to eliminate the noise. Also we are just passing the key sentence which defines the correct category instead of sending whole mail body. Even after taking utmost care to eliminate the noise for training the model, still the F-Score is decreasing instead of increasing. It went from 0.45 to 0.01 very fast. Now the category itself is not getting detected.

-What could be the root case of this issue?

-Are there any additional steps which i can follow to improve the FScore. 

- What is the process if i want to import the model from higher env to lower env, delete the unwanted feedback from model in lower env, and then upload this cleaned up model back to higer env?

- Will the above step improve the fscore?

Pega Platform 8.1.8 Decision Management Manufacturing and High Technology Lead System Architect