Balaji padmanabhan asu thesis statement


balaji padmanabhan asu thesis statement

10 iterations: 100 iterations: Type 2 (this grid is for doc set with type id 5 from. Mostly the topics make at least some sense but many of those coherence measures show higher values for bigger numbers. So if I want to apply topic models, what would I do right now (NLP is getting lots of attention so who knows in a few years.)? If I needed to model large numbers of separate sets that are evolving over time, I might just use the cohesion metrics along with some heuristics (e.g., number of docs vs number of topics) to make automated choices, run the things as micro-services at intervals. Type 3 refers to models where there was no big difference in final topic diff in 10 vs 100 iterations. The machine I ran it on has 32GB RAM and a quad-core Core i7 processor (hyperthreads to 8 virtual cores). Tune as needed over time. Unless maybe if you want to capture really fine grained differences in topics. To see a large number of topics at once vs cycling through one at a time. Seems reasonable given the smallish number of documents I have. So I plotted the topic diff for the wikipedia run (when generating the LDA models to see how much the topics drift during the run. Maybe somewhat equal to iterations of old.

balaji padmanabhan asu thesis statement

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Implemented LDA in Java back then based on that tutorial. Both for default parameters and autotuned parameters. Unfortunately, I am not paid for this and have too many other things. Have to say, maybe not very excited. Wikipedia example: This dumps the whole LDAvis thing into a html file you can then load up any time later and play with. Code: And to plot it: And the results for each of the document sets: Doc set id 10 iterations 100 iterations So how does all this feel when I load the topics up and look at them? Buy the Full Version. Buy the Full Version, you're Reading a Free Preview, pages 225 to 230 are not shown in this preview. Not in my scope to investigate further, but the reasons could be anything, what do I know. And the Gensim docs also nicely describe how running this online algorithm now also merges the results in a way that you dont necessarily need to run large numbers of passes (iterations) over the corpus to converge on a better model. A handy tool for topic exploration. See where that takes.

So if the smaller number of topics would be better, maybe I need to try even smaller number of topics. Gensim nicely comes with a script to parse it for dictionary and corpus: python -m ke_wiki, then some code to build different sizes of topic models (25 to 200 topics in 25 topic size increments). Or maybe I am just bad at using stuff. I am sure this would also be an interesting topic to study, why PCA grounds them together.


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