[[stemming]] == Reducing Words to Their Root Form Most languages of the world are _inflected_, meaning ((("languages", "inflection in")))((("words", "stemming", see="stemming words")))((("stemming words")))that words can change their form to express differences in the following: * _Number_: fox, foxes * _Tense_: pay, paid, paying * _Gender_: waiter, waitress * _Person_: hear, hears * _Case_: I, me, my * _Aspect_: ate, eaten * _Mood_: so be it, were it so While inflection aids expressivity, it interferes((("inflection"))) with retrievability, as a single root _word sense_ (or meaning) may be represented by many different sequences of letters.((("English", "inflection in"))) English is a weakly inflected language (you could ignore inflections and still get reasonable search results), but some other languages are highly inflected and need extra work in order to achieve high-quality search results. _Stemming_ attempts to remove the differences between inflected forms of a word, in order to reduce each word to its root form. For instance `foxes` may be reduced to the root `fox`, to remove the difference between singular and plural in the same way that we removed the difference between lowercase and uppercase. The root form of a word may not even be a real word. The words `jumping` and `jumpiness` may both be stemmed to `jumpi`. It doesn't matter--as long as the same terms are produced at index time and at search time, search will just work. If stemming were easy, there would be only one implementation. Unfortunately, stemming is an inexact science that ((("stemming words", "understemming and overstemming")))suffers from two issues: understemming and overstemming. _Understemming_ is the failure to reduce words with the same meaning to the same root. For example, `jumped` and `jumps` may be reduced to `jump`, while `jumping` may be reduced to `jumpi`. Understemming reduces retrieval relevant documents are not returned. _Overstemming_ is the failure to keep two words with distinct meanings separate. For instance, `general` and `generate` may both be stemmed to `gener`. Overstemming reduces precision: irrelevant documents are returned when they shouldn't be. .Lemmatization ********************************************** A _lemma_ is the canonical, or dictionary, form ((("lemma")))of a set of related words--the lemma of `paying`, `paid`, and `pays` is `pay`. Usually the lemma resembles the words it is related to but sometimes it doesn't -- the lemma of `is`, `was`, `am`, and `being` is `be`. Lemmatization, like stemming, tries to group related words,((("lemmatisation"))) but it goes one step further than stemming in that it tries to group words by their _word sense_, or meaning. The same word may represent two meanings&#x2014;for example,_wake_ can mean _to wake up_ or _a funeral_. While lemmatization would try to distinguish these two word senses, stemming would incorrectly conflate them. Lemmatization is a much more complicated and expensive process that needs to understand the context in which words appear in order to make decisions about what they mean. In practice, stemming appears to be just as effective as lemmatization, but with a much lower cost. ********************************************** First we will discuss the two classes of stemmers available in Elasticsearch&#x2014;<<algorithmic-stemmers>> and <<dictionary-stemmers>>&#x2014;and then look at how to choose the right stemmer for your needs in <<choosing-a-stemmer>>. Finally, we will discuss options for tailoring stemming in <<controlling-stemming>> and <<stemming-in-situ>>.