What is a Natural Language Query?
A natural language query (NLQ) is a free-form, conversational input like “men’s waterproof trail shoes under €150 in size 45”. NLQ systems parse intent and constraints (gender, category, attribute, price, size) and convert them into structured search actions.
How It Works (quick)
- Parse: Detect intent, entities (brand, size, color), and operators (≤, ≥, “under”, “before”).
- Normalize: Units/currency, sizes, date ranges, synonyms (trainers ↔ sneakers).
- Translate: Build a structured query: facets/filters + keywords + boosts.
- Retrieve & rank: Hybrid recall (lexical + vectors) → re-rank with quality/business signals.
- Explain: Show chips/breadcrumbs (“Size 45”, “Waterproof”, “≤€150”) and allow quick edits.
Why It Matters in E-commerce
- Fewer steps: One query can apply multiple filters at once.
- Higher satisfaction: Shoppers feel understood and reach viable items faster.
- Accessibility: Works better on mobile and for non-expert users.
Best Practices
- Operator grammar: Map words like “under/over/before/near me” to operators and fields.
- Disambiguation: Ask a single clarifying question when needed (e.g., men vs women).
- Safety: Enforce hard rules (in-stock, ACL, region) regardless of phrasing.
- Transparency: Render extracted constraints as editable chips.
- Localization: Handle locale-specific sizes, currencies, and slang.
- Analytics: Log parse success, corrections, and drop-offs.
Challenges
- Ambiguous phrasing, code-switching, noisy inputs, and unit/size conversions.
Examples
- “nike gore-tex trail shoes size 45 under 150” → brand:Nike + waterproof + category:trail + size:45 + price ≤150.
- “return policy for gifts” → route to help article with the right section highlighted.
Summary
NLQ turns everyday language into structured filters and keywords. Keep it transparent, localized, and safe—so one query gets users straight to what they need.
