What is Concept Extraction?
Concept extraction (a.k.a. keyphrase/term extraction) identifies salient entities, attributes, and topics in text and maps them to usable labels. It feeds facets, filters, schema, and recommendations without hand-tagging every item.
How It Works (quick)
- Methods: Keyword scoring (TF-IDF/YAKE/RAKE), sequence models (BiLSTM/CRF/Transformers), and embedding-based term mining.
- Normalization: Canonicalize terms (case/diacritics), singular/plural, and join hyphen variants.
- Linking: Map extracted terms to a controlled vocabulary/taxonomy with IDs; disambiguate senses.
- Scoring & thresholds: Keep high-confidence concepts; route low-confidence to human review.
- Outputs: Write to structured fields for search, facets, and schema markup.
Why it Matters in E-commerce
- Better facets & recall: Pull brand, material, fit, use-case from titles/descriptions for reliable filtering.
- Less manual work: Auto-tags speed catalog onboarding.
- Richer SEO: Populate structured data and internal links from concepts.
Best Practices
- Maintain a controlled vocabulary with preferred labels and synonyms.
- Use locale-specific analyzers; don’t force one tokenizer across markets.
- Keep confidence thresholds and a review queue for risky concepts.
- Log explanations (e.g., matched spans) for auditability.
- Retrain with feedback; version models and vocabularies.
Challenges
- Ambiguity: “Apple” brand vs fruit; “tee” vs “t-shirt”.
- Noise: Vendor boilerplate and marketing fluff.
- Drift: New brands/styles emerge; keep vocabulary fresh.
Examples
- Extract “GORE-TEX”, “trail running”, “merino” from copy to power filters and PDP badges.
- Tag help articles with shipping, returns, size guide to route Best Bets.
Summary
Concept extraction turns messy text into structured, searchable labels. With a good vocabulary, thresholds, and review, it boosts filters, SEO, and recommendations while cutting manual tagging.
