Hi NL, thx for giving our hack a spin. We are working on it. Next version will have auto generated feature trees which will result in much better results.
thx, recognized the variance, two things: results are based on a tech domain specific vector (based on 1.2m tech articles / 20y), so non-techs fall off. We source content on requested entities live. Descriptions of younger entities are more concise/less global -> more valid classification. But then again, our assumption was that there is more demand for classification of little known entities than for F100s. But we are here to validate/falsify that assumption.
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[ 4.3 ms ] story [ 24.5 ms ] threadIt seems to work somewhat for SV startup companies, but fails pretty badly on others.
Eg:
IBM
https://www.getcontext.io/classifier/#/about/ibm: Evenly distributed between Augmented Reality/Cloud Computing/Cloud Infrastructure/Entertainment/Fintech/HR
Google
https://www.getcontext.io/classifier/#/about/google - apparently about virtual currency and crypto currency as much as a search engine.
Ford: No result
GE: Serves results for GEICO
But as nl stated, not that accurate for some companies.
Would be great if you could describe in more detail how you create the classification.