Ask HN: Are engineers afraid of free text?
Text classification isn't new. Yet, very few product & eng teams actually do text classification.
Do you build text classification models in house? Why do you think eng teams with 'obvious' use cases don't?
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: * The people or industry have low tolerance or fear around risk of false positives * The industry is centered around billable hours and has no incentive for automation * The engineers or people perceive ML as this obscure/difficult thing 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. [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. > Yet, very few product & eng teams actually do text classification. This is an extremely incorrect assumption. Example? To clarify, I'm referring to product eng and SWEs. If you have a Data / ML team, they will handle ofc.