Keyword Extraction — Complete Guide
Keyword Extraction — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 64 of 100
Keyword Extraction
Foundations ✓ → Models ✓ → NLP & advanced → MLOps
NLP & advanced · 3 — Recs, text, ONNX · ~10 min · Module 7: NLP with ML.NET
What is this?
Keyword extraction surfaces important terms from documents — via TF-IDF, n-grams, or custom maps.
Why should you care?
AIPredict search tuning and fraud note triage need top terms without manual tagging.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var keywords = ml.Transforms.Text.ProduceWordBags("Bag", "Description", ngramLength: 2)
.Append(ml.Transforms.SelectFeaturesBasedOnCount("TopTerms", "Bag", maximumNumberOfInfluences: 50));
var fitted = keywords.Fit(corpus);
var top = ml.Data.CreateEnumerable<BagRow>(fitted.Transform(sample), reuseRowObject: false)
.First().TopTerms.Where(w => w > 0).Take(10);What happened?
- Word bags + count-based selection yield compact term weights.
- Use for search boosts or human review queues.
Practice next
- ProduceWordBags bigrams.
- SelectFeaturesBasedOnCount.
- Inspect top terms per doc.
- Change maximumNumberOfInfluences.
- Join terms to fraud category rules.
Remember
N-gram bags. Count-based trim. Human-readable terms.
AIPredict dispute keywords
Chargeback notes surface "refund" and "duplicate".
Outcome: Analysts route cases faster.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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