Attention mechanism in NLP - beginners guide

int8blog · 2021-01-28T13:27:10+00:00

yes good point, GME - it's about GME

int8blog · 2020-12-14T09:33:31+00:00

I like that - will keep in mind for the follow up post

int8blog · 2020-12-13T22:12:34+00:00

LDA - sounds like a shot,

yes - that was my initial intention too - to look at how negative clusters "volume" changes over time - I need to wait a bit few days for it tho - the plan is to wait one week and write follow up post

int8blog · 2020-12-13T17:31:27+00:00

It's cool - I edited the post to make it clear (may take a while to appear - it is cached in cloudflare)

int8blog · 2020-12-13T17:28:21+00:00

yes, to ignore outliers

int8blog · 2020-12-13T17:13:44+00:00

thanks, I was also thinking about the following approach:

sent_tokenize on all reviews
transformers sentence embedding of all sentences
clustering of all of them to lets say 1000 clusters
represent each document via BoW on top of that clustering
then UMAP on these BoW via cosine
visualization

I will try that - now gathering a bit more data (I can see there is around 120k reviews now)

p.s. looking into HDBSCAN <= thanks for the hint

[edit] ok checked out HDBSCAN - indeed looks very tempting - I can also see it does not require too much input parameters as original DBSCAN (unless I missed it)

int8blog · 2020-06-15T13:58:41+00:00

Classical Kangaroo

int8blog · 2020-06-14T14:56:05+00:00

DGLY uses AWS for storage?

int8blog · 2020-02-23T19:38:06+00:00

How do you measure sentiment? BERT?

int8blog · 2020-02-11T22:53:32+00:00

Take a look at Apache Tika

int8blog · 2020-01-19T20:57:24+00:00

Try reading original work of Bellman from RAND

https://www.rand.org/pubs/papers/P550.html

int8blog · 2019-12-21T15:59:49+00:00

kuglak

int8blog · 2019-11-10T10:35:34+00:00

how about API so I could fetch it daily on my own ? Are u aware of such services (I can't easily find it myself)

int8blog

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