--- title: Stemming description: Stemming is the process of reducing a word to its root form. This ensures variants of a word match during a search. For example, walking and walked can... url: https://www.elastic.co/elastic/docs-builder/docs/3016/manage-data/data-store/text-analysis/stemming products: - Elasticsearch applies_to: - Elastic Cloud Serverless: Generally available - Elastic Stack: Generally available --- # Stemming *Stemming* is the process of reducing a word to its root form. This ensures variants of a word match during a search. For example, `walking` and `walked` can be stemmed to the same root word: `walk`. Once stemmed, an occurrence of either word would match the other in a search. Stemming is language-dependent but often involves removing prefixes and suffixes from words. In some cases, the root form of a stemmed word may not be a real word. For example, `jumping` and `jumpiness` can both be stemmed to `jumpi`. While `jumpi` isn’t a real English word, it doesn’t matter for search; if all variants of a word are reduced to the same root form, they will match correctly. ## Stemmer token filters In Elasticsearch, stemming is handled by stemmer [token filters](/elastic/docs-builder/docs/3016/manage-data/data-store/text-analysis/anatomy-of-an-analyzer#analyzer-anatomy-token-filters). These token filters can be categorized based on how they stem words: - [Algorithmic stemmers](#algorithmic-stemmers), which stem words based on a set of rules - [Dictionary stemmers](#dictionary-stemmers), which stem words by looking them up in a dictionary Because stemming changes tokens, we recommend using the same stemmer token filters during [index and search analysis](https://www.elastic.co/elastic/docs-builder/docs/3016/manage-data/data-store/text-analysis/index-search-analysis). ## Algorithmic stemmers Algorithmic stemmers apply a series of rules to each word to reduce it to its root form. For example, an algorithmic stemmer for English may remove the `-s` and `-es` suffixes from the end of plural words. Algorithmic stemmers have a few advantages: - They require little setup and usually work well out of the box. - They use little memory. - They are typically faster than [dictionary stemmers](#dictionary-stemmers). However, most algorithmic stemmers only alter the existing text of a word. This means they may not work well with irregular words that don’t contain their root form, such as: - `be`, `are`, and `am` - `mouse` and `mice` - `foot` and `feet` The following token filters use algorithmic stemming: - [`stemmer`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-stemmer-tokenfilter), which provides algorithmic stemming for several languages, some with additional variants. - [`kstem`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-kstem-tokenfilter), a stemmer for English that combines algorithmic stemming with a built-in dictionary. - [`porter_stem`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-porterstem-tokenfilter), our recommended algorithmic stemmer for English. - [`snowball`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-snowball-tokenfilter), which uses [Snowball](https://snowballstem.org/)-based stemming rules for several languages. ## Dictionary stemmers Dictionary stemmers look up words in a provided dictionary, replacing unstemmed word variants with stemmed words from the dictionary. In theory, dictionary stemmers are well suited for: - Stemming irregular words - Discerning between words that are spelled similarly but not related conceptually, such as: - `organ` and `organization` - `broker` and `broken` In practice, algorithmic stemmers typically outperform dictionary stemmers. This is because dictionary stemmers have the following disadvantages: - **Dictionary quality** A dictionary stemmer is only as good as its dictionary. To work well, these dictionaries must include a significant number of words, be updated regularly, and change with language trends. Often, by the time a dictionary has been made available, it’s incomplete and some of its entries are already outdated. - **Size and performance** Dictionary stemmers must load all words, prefixes, and suffixes from its dictionary into memory. This can use a significant amount of RAM. Low-quality dictionaries may also be less efficient with prefix and suffix removal, which can slow the stemming process significantly. You can use the [`hunspell`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-hunspell-tokenfilter) token filter to perform dictionary stemming. If available, we recommend trying an algorithmic stemmer for your language before using the [`hunspell`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-hunspell-tokenfilter) token filter. ## Control stemming Sometimes stemming can produce shared root words that are spelled similarly but not related conceptually. For example, a stemmer may reduce both `skies` and `skiing` to the same root word: `ski`. To prevent this and better control stemming, you can use the following token filters: - [`stemmer_override`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-stemmer-override-tokenfilter), which lets you define rules for stemming specific tokens. - [`keyword_marker`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-keyword-marker-tokenfilter), which marks specified tokens as keywords. Keyword tokens are not stemmed by subsequent stemmer token filters. - [`conditional`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-condition-tokenfilter), which can be used to mark tokens as keywords, similar to the `keyword_marker` filter. For built-in [language analyzers](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-lang-analyzer), you also can use the [`stem_exclusion`](https://docs-v3-preview.elastic.dev/elastic/docs-builder/docs/3016/reference/text-analysis/analysis-lang-analyzer#_excluding_words_from_stemming) parameter to specify a list of words that won’t be stemmed.