- 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 with retrievability, as a single root word sense (or meaning) may be represented by many different sequences of letters. 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
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
The root form of a word may not even be a real word. The words
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
Understemming is the failure to reduce words with the same meaning to the same
root. For example,
jumps may be reduced to
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.
generate may both be stemmed to
Overstemming reduces precision: irrelevant documents are returned when they
First we will discuss the two classes of stemmers available in Elasticsearch—Algorithmic Stemmers and Dictionary Stemmers—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.