Lemmatisation
Reducing a word to its base dictionary form (lemma). Used by search engines to broaden queries.
In brief
Lemmatisation is the process of reducing a word to its base (dictionary) form, or lemma. For example, 'running', 'ran', 'runs' → lemma 'run'. Search engines use lemmatisation to find pages that contain any grammatical form of a keyword, not just the exact match.
What is lemmatisation
Lemmatisation considers context and part of speech. For example, 'better' → lemma 'good'. This allows search engines to understand that 'good', 'great', 'excellent' may be related, but they are not always exact synonyms.
Lemmatisation vs stemming
- Stemming — crude removal of suffixes (e.g., 'running' → 'runn'). Faster but less accurate.
- Lemmatisation — reduces to dictionary form using morphology ('running' → 'run'). Slower but more accurate.
Example: 'better' → stemming gives 'bet', lemmatisation gives 'good'.
How lemmatisation is used in SEO
- Text analysis – check if a page is over‑optimised for one word form.
- Semantic core expansion – extend keyword lists by adding different word forms.
- Clustering – group queries that share a common lemma.
- Intent understanding – lemmatisation helps extract the core topic from queries.
Search engines (Google, Yandex) perform lemmatisation on the query themselves. You do not need to stuff all word forms on your page – just write naturally. Lemmatisation is more relevant when collecting semantics and analysing competitors.
FAQ
Common questions
No, that would be over‑optimisation. Write naturally; algorithms will understand the root and lemma. It‘s enough that the word appears in different forms organically.
Yes, but quality varies. For English and Russian, morphological analysers are good. For languages with rich morphology (Finnish, Hungarian), it may be harder.
Yes, it is one method of soft clustering. However, it is less reliable than SERP‑based clustering. Better to combine both approaches.
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