What is Semantic Analysis?
Semantic analysis extracts and represents meaning beyond exact words. It includes embeddings (dense vectors), entity/relation extraction, word-sense disambiguation, topic/intent classification, and linking to a knowledge graph.
How It Works (quick)
- Prep: Language detection, normalization, lemmatization.
- Represent: Create embeddings for queries/products; compute similarity.
- Understand: Extract entities/attributes (brand, material, size), disambiguate senses, detect intent/topics.
- Link: Map concepts to taxonomy/KG IDs for consistent filters and collections.
- Serve: Hybrid retrieval (BM25 + vectors) → neural re-rank → business-aware ranking.
Why It Matters in E-commerce
- Vocabulary gap: Matches “rainproof trail runners” to waterproof trail shoes.
- Attribute enrichment: Converts vendor text into reliable facets (e.g., GORE-TEX, EU 45).
- Better answers: Connects PDPs with guides, policies, and compatibility information.
Best Practices
- Domain-tune models with your catalog and queries.
- Keep generation grounded in retrieved text; store sources and spans.
- Locale support: per-language models or adapters; align sizes/units.
- Pair semantics with exact fields for brand/SKU precision.
- Evaluate with NDCG, coverage, and manual audits; monitor drift.
- Governance: Version models, keep fallbacks, and log feature contributions.
Challenges
- Cost/latency, multilingual nuance, stale embeddings, and explainability for deep models.
Examples
- Query “breathable shell that packs small” → semantic recall surfaces packable windproof jackets.
- Extract compatibility: Laptop ↔ RAM module, Phone ↔ case.
Summary
Semantic analysis adds meaning to your stack—bridging wording gaps, enriching attributes, and powering hybrid search and answers under solid guardrails.
