{"id":33435,"date":"2026-11-14T20:53:43","date_gmt":"2026-11-14T19:53:43","guid":{"rendered":"https:\/\/wooacademy.sk\/?p=33435"},"modified":"2026-11-14T20:53:43","modified_gmt":"2026-11-14T19:53:43","slug":"stemming-vs-lemmatization-ecommerce-search","status":"publish","type":"post","link":"https:\/\/wooacademy.sk\/en\/stemming-vs-lemmatization-ecommerce-search\/","title":{"rendered":"Stemming vs lemmatization: why your store misses other word forms"},"content":{"rendered":"\n

Stemming chops the ending off a word and keeps the stem (chargers, charging, charged \u2192 charg). Lemmatization finds the dictionary base form (chargers \u2192 charger, ran \u2192 run).<\/strong> Both make search find a product regardless of the number, case or tense the shopper uses. Stemming is fast and simple; lemmatization is more accurate but needs a dictionary and more computation.<\/p>\n\n

Why it matters more for Slovak and Czech stores than for English ones<\/h2>\n

English has a handful of endings (shoe, shoes). Slovak and Czech have seven cases, three genders and dozens of declension patterns. One noun commonly has more than ten forms. A shopper types “nab\u00edja\u010dky na mobil” (phone chargers), the product is called “Nab\u00edja\u010dka na mobil USB-C”. Without handling word forms, “nab\u00edja\u010dky” and “nab\u00edja\u010dka” are two different words to the computer.<\/p>\n

Default WordPress and WooCommerce search looks for text with an SQL LIKE condition. A partial string would match both, but the full word “nab\u00edja\u010dky” does not find a product that only has “nab\u00edja\u010dka” in its title. In English the same happens with irregular plurals and with queries such as “batteries” against “battery”.<\/p>\n\n

Stemming and lemmatization compared<\/h2>\n\n\n
<\/th>Stemming<\/th>Lemmatization<\/th><\/tr><\/thead>\n
Principle<\/td>Rules chop off endings<\/td>Dictionary and morphological analysis return the base form<\/td><\/tr>\n
Output<\/td>A stem, not necessarily a real word<\/td>A real word (lemma)<\/td><\/tr>\n
Speed<\/td>Very fast<\/td>Slower<\/td><\/tr>\n
Errors<\/td>Sometimes joins words that do not belong together or misses irregular forms<\/td>Fewer errors if the dictionary knows the word<\/td><\/tr>\n
Best for<\/td>Real-time search, autocomplete<\/td>Text analysis, search engines with large budgets<\/td><\/tr>\n<\/tbody>\n<\/table>\n

The Stanford textbook Introduction to Information Retrieval describes it the same way: stemming is a crude heuristic that chops off the ends of words, lemmatization uses a vocabulary and morphological analysis to return the dictionary form.<\/p>\n\n

Where stemming goes wrong and how to work around it<\/h2>\n