Phrase Extraction

What is Phrase Extraction?

Phrase extraction identifies salient multi-word expressions (MWEs) and keyphrases from text—e.g., “merino base layer”, “USB-C fast charger”. Methods range from linguistic patterns (noun phrases) to statistics (PMI, C-value), graph/ranking (TextRank-style), and embedding similarity.

How It Works (quick)

• Prep: Clean text, detect language, lemmatize; protect brand/SKU casing.
• Candidate generation: Noun-phrase patterns, collocations (bigrams/trigrams), sliding windows.
• Scoring: Frequency, positional bias, TF-IDF/PMI/C-value, or vector relevance to domain topics.
• Filtering: Stoplists, de-dup by lemma, length limits, headword rules.
• Linking: Map phrases to taxonomy nodes or knowledge-graph IDs.
• Output: Tags/attributes for search, facets, and analytics.

Why It Matters in E-commerce

• Better discovery: Auto-tags feed facets, collections, and product finders.
• SEO: Surfaces natural long-tail topics for category/collection pages.
• Ops: Extract specs from vendor copy to fill attributes quickly.

Best Practices

• Keep a domain lexicon (materials, fits, compatibilities) and refresh quarterly.
• Normalize hyphens/diacritics; merge singular/plural via lemmas.
• Whitelist phrases that map to filterable attributes; blacklist vague marketing fluff.
• Human-in-the-loop: review low-confidence phrases; store spans & confidence.
• Localize per market; handle compounds (e.g., German) carefully.

Challenges

• Noisy UGC, near-duplicates (“tee shirt” vs “T-shirt”), trademark sensitivity, mixed languages.

Examples

• Extract “GORE-TEX membrane”, “packable hood”, “EU 45”, “USB-C PD 65W” from titles/descriptions.
• Turn frequent phrases into guided search chips or collection pages.

Summary

Phrase extraction converts messy text into actionable, multi-word tags that fuel facets, collections, and relevance—especially valuable for long-tail demand.