A step-by-step deriviation of collapsed Gibbs sampling of LDA

How we can derive the collapsed Gibbs sampling procedure for LDA from scratch: a step by step illustration. This is based on Wang Yi's tech report.

Relationship between topic modeling and documents clustering

I am learning to use topic modeling for documents clustering. I would like to clarify whether my understaning of the relationship between latent dirichlet allocation (LDA) and the generic task of document clustering is
correct or not?

The LDA analysis tends to output the topic proportions for each document. This is not the direct result of document clustering. However, we can treat this probability proportions as a feature reprsentation for each document. Afterwards, we can invoke other established clustering method, like K-means, to cluster documents based on the feature configurations generated by LDA analysis.

The best metric we found for computing the semantic similarity of topics was a pairwise topic coherence, using the coherence metric from "Automatic Evaluation of Topic Coherence," by Newman et al., NAACL 2010.

關於鍵盤異常

有時候鍵盤會發生特殊情形，如 T 鍵變win + T，F鍵變成win + F，等等…，這個問題常發生在使用虛擬機或遠端登入後，鍵盤的Fn鍵被開啟，這時只要再按一下 win + 任意功能鍵，就會恢復正常了