TECH Signal 411
Categorization with NLP
This is a practical example of solving a classification problem without training data or ML infrastructure, which is relevant for small projects where data collection isn't feasible. The approach—normalize input to sorted word stems, then match against a manually curated ordered term list with bigram disambiguation—shows a concrete tradeoff between model complexity and maintainability. Only one feed carried this, so it's a single-author writeup inviting peer critique rather than a widely validated technique.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
The pipeline lowercases input, normalizes to NFD Unicode form to strip accents, tokenizes into words, applies Porter stemming, and sorts the resulting stems to produce a stable lookup key.
A CSV database of unigrams and bigrams with two precedence groups—derivations (e.g., juice, milk, oil) ranked above raw ingredients (e.g., apple, olive, oat)—resolves conflicts where a single word is ambiguous.
Bigrams handle multi-word terms like "spaghetti squash" or "apple sauce" that no single unigram can correctly categorize, with bigrams checked before unigrams within each precedence group.
THE CLUSTER