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ML-Powered Search & Ranking • Query Understanding (Intent, Parsing, Rewriting)Easy⏱️ ~2 min

What is Query Understanding in Search Systems?

Definition
Query understanding is the process of interpreting what a user actually wants from their search query. It transforms raw text into structured signals: intent (what type of result?), entities (what specific things?), and reformulations (how to improve the query?).
Why Raw Queries Fail
Users type "apple" but mean the company, the fruit, or the record label. They type "cheap flights NYC" when they want "inexpensive airfare to New York City." They misspell, use abbreviations, and omit context obvious to them but invisible to the system. Without query understanding, search returns literal keyword matches that miss 30-50% of relevant results and include 20-30% irrelevant ones.
The Three Core Tasks
Intent classification: Determine the query type (navigational, informational, transactional) to route to appropriate backends. Entity extraction: Identify and link mentions to known entities ("NYC" → New York City, population 8.3M, coordinates 40.7°N). Query rewriting: Expand, correct, or reformulate queries to improve recall ("ML" → "machine learning") or precision (adding implicit filters).
Impact on Search Quality
Good query understanding improves relevance metrics by 15-30%. It reduces zero-result queries by 40-60% through spell correction and synonym expansion. It enables personalization by connecting queries to user context. Without it, even perfect ranking algorithms fail because they rank the wrong candidate set.

💡 Key Takeaways

✓Query understanding transforms raw text into structured signals: intent, entities, reformulations

✓Raw keyword matching misses 30-50% relevant results and includes 20-30% irrelevant ones

✓Three core tasks: intent classification, entity extraction, query rewriting

✓Good query understanding improves relevance 15-30% and reduces zero-result queries 40-60%

✓Even perfect ranking fails without query understanding - you rank the wrong candidate set

📌 Interview Tips

1Explain the three core tasks (intent, entities, rewriting) as a structured framework

2Cite specific impact numbers (15-30% relevance improvement, 40-60% zero-result reduction)

3Use the "apple" ambiguity example to illustrate why raw queries fail

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