You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository was archived by the owner on Sep 11, 2020. It is now read-only.
I have experimented with adjustments to the 'lda_loss' function:
E.g. Lda2vec.py:
This change to the lda-loss learning algorithm reduces the correlation between topics in the topic_embedding matrix.
Also, this NIPS paper discusses a methodology for quantifying LDA performance, specifically, by measuring: word intrusion and topic intrusion.
http://users.umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf
Please experiment and let me know what you find.
Topic Similarity Matrix after 33 Epochs:

The text was updated successfully, but these errors were encountered: