Text classification isn't new. Yet, very few product & eng teams actually do text classification.<p>Do you build text classification models in house? Why do you think eng teams with 'obvious' use cases don't?<p>Some examples below:
- LegalTech categorizing legal docs for lawyers
- HR Tech tagging job descriptions by category
- Insurance labeling docs/records for claims adjusters
There can be multiple reasons for this[0], including but not limited to:<p>* The people or industry have low tolerance or fear around risk of false positives<p>* The industry is centered around billable hours and has no incentive for automation<p>* The engineers or people perceive ML as this obscure/difficult thing<p>I'd say the incentives and risks have hindered lots of legal adoption (this is what I observed while working in legaltech for instance). Insurance sounds similar, but I'm less familiar and assume they are coming along more quickly.<p>[0] I agree with minimaxir's point, that it's a bad assumption to think few teams use basic ML functionality. This will become even more true as emergent tech such as zero shot classification with LLMs becomes more commoditized.