What is NLP?
Natural Language Processing (NLP) is the field of AI that enables systems to analyze, understand, and produce human language. Typical tasks include tokenization, lemmatization, entity extraction, classification, summarization, semantic matching, and question answering.
How It Works (quick)
- Preprocessing: Language detection, tokenization, lemmatization, normalization (case/diacritics).
- Understanding: Entity and concept extraction, intent/classification, sentiment, topic modeling.
- Representation: Embeddings (dense vectors) capture semantic meaning; keyword features capture lexical match.
- Generation: Summaries, titles, or FAQ answers (grounded in trusted content).
- Serving: Combine lexical (BM25) and semantic signals; enforce security and stock rules.
Why It Matters in E-commerce
- Better search: Understands synonyms, variants, and vague queries.
- Richer discovery: Auto-tags products and connects them to guides and FAQs.
- Support at scale: Surfaces policy answers and order info reliably.
- Personalization: Learns preferences from queries and content interactions.
Best Practices
- Hybrid retrieval: Pair keyword and vector search; re-rank for quality.
- Grounded generation: Cite sources; avoid free-form hallucinations.
- Localization: Per-locale analyzers, synonyms, and units.
- Privacy & safety: Mask PII, respect ACLs/consent, log model decisions.
- Measurement: Track CTR, conversion, zero-results, answer accuracy, latency.
Challenges
- Multilingual complexity, bias, drift, latency/cost, and data quality issues.
Examples
- “vegan leather tote with zipper” returns the right collection plus a sizing guide.
- Help-center search answers “return period” with the exact policy excerpt.
Summary
NLP turns messy language into structured meaning and helpful answers—powering accurate search, tagging, and support when paired with guardrails.
