I rarely comment on videos, but I greatly appreciate short, focused introductions on methods before committing hours to diving in further to applications, methods and alternatives. Thank you!
13:11 did you mean to say "the next step is to now look at the 'word' distribution beta corresponding to whatever topic assignment we use.."? Beta is a distribution over words and not topics
I think we're both correct! \beta is often called itself a topic, so it probably would have been best to say "that topic's distribution over words" to avoid this confusion. Thanks for the opportunity to clarify that!
@10:14 its slightly confusing. You mentions that there are two Dirichlet distributions. It seems that what your pointing at ( topic distributions over words ) is just a collection of multinomial distributions and not a distribution over distributions (Dirichlet)
I try to stay away from cutting edge research because it changes so fast. But: 1) we are doing research in this area, and thanks to Corona all of our research talks become TH-cam videos, so we may have something 2) we do have some new topic models videos, including touching on deep learning soon.
@@JordanBoydGraber Thank you. That's great, I'll look out for it. It's difficult to find documentation on newer models apart from the research papers about them and sometimes it is difficult to understand everything reading those...or at least for me as a student/intermediate beginner.😅 Have a great day and please continue doing TH-cam, you've already helped me so much over the years!
Excellent point! These are just made up. There are some automatic approaches to label topics though: medium.com/datadriveninvestor/automatic-topic-labeling-in-2018-history-and-trends-29c128cec17
![[MV] A leap of faith...ความเชื่อใจครั้งสุดท้าย (From "Petrichor the series")](http://i.ytimg.com/vi/U1piZH2CNXA/mqdefault.jpg)
I rarely comment on videos, but I greatly appreciate short, focused introductions on methods before committing hours to diving in further to applications, methods and alternatives. Thank you!
Excellent series. Best explanation I’ve seen for LDA and topic modeling. Thank you very much.
This video should be an example of how you teach properly using video tutorials
13:11 did you mean to say "the next step is to now look at the 'word' distribution beta corresponding to whatever topic assignment we use.."? Beta is a distribution over words and not topics
I think we're both correct! \beta is often called itself a topic, so it probably would have been best to say "that topic's distribution over words" to avoid this confusion.
Thanks for the opportunity to clarify that!
@10:14 its slightly confusing. You mentions that there are two Dirichlet distributions. It seems that what your pointing at ( topic distributions over words ) is just a collection of multinomial distributions and not a distribution over distributions (Dirichlet)
Very nice presentation
"LDA? I'm suing!" - Ronald Fisher
This is so confusing. Mike Jordan and Dave Blei should have known better.
Would love to see a video on the Embedded Topic Model or Dynamic Embedded Topic Model by Adji Dieng et al. next :)
I try to stay away from cutting edge research because it changes so fast. But: 1) we are doing research in this area, and thanks to Corona all of our research talks become TH-cam videos, so we may have something 2) we do have some new topic models videos, including touching on deep learning soon.
@@JordanBoydGraber Thank you. That's great, I'll look out for it. It's difficult to find documentation on newer models apart from the research papers about them and sometimes it is difficult to understand everything reading those...or at least for me as a student/intermediate beginner.😅 Have a great day and please continue doing TH-cam, you've already helped me so much over the years!
Can you please share link to this slide. This is the best video on Topic modelling
Thanks for the kind words, here are the slides:
users.umiacs.umd.edu/~jbg/teaching/CMSC_726/16a.pdf
I have a doubt:if one document is given as input , how do we get names of the topics
Excellent point! These are just made up. There are some automatic approaches to label topics though:
medium.com/datadriveninvestor/automatic-topic-labeling-in-2018-history-and-trends-29c128cec17
its copy of an other video
I prefer to think of it as an improvement/update of an older video. However, you see more of my face, so you might feel the opposite. :)