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How to turn messy text into useful signals (without deep learning)
13 min read13 hours ago
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If you work with text, you often need one simple thing: Which words actually matter in each document?
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tfidf_query_ranking.gif
However, raw word counts fail quickly. Common words dominate, and short texts behave differently from long texts. Therefore, we need a weighting method that rewards specific words and down-weights common ones.
TF-IDF is a classic solution. It is not fancy, but it is reliable, fast, and easy to verify. As a result, it is still widely used for search, tagging, clustering, and as a baseline before large language models.
The realistic problem
Imagine a support team that receives t…
Member-only story
How to turn messy text into useful signals (without deep learning)
13 min read13 hours ago
–
If you work with text, you often need one simple thing: Which words actually matter in each document?
Press enter or click to view image in full size
tfidf_query_ranking.gif
However, raw word counts fail quickly. Common words dominate, and short texts behave differently from long texts. Therefore, we need a weighting method that rewards specific words and down-weights common ones.
TF-IDF is a classic solution. It is not fancy, but it is reliable, fast, and easy to verify. As a result, it is still widely used for search, tagging, clustering, and as a baseline before large language models.
The realistic problem
Imagine a support team that receives thousands of customer tickets. Each ticket is short, unstructured text.
You want two things:
- Find similar tickets (so agents can reuse solutions).
- Rank tickets for a search query like billing charged refund.
Constraints are real:
- Tickets are short, often messy, and full of repeated words like app, order, issue.
- You need explainability: you must answer why this ticket matched.
- You want speed, because this can run on every new ticket.