While you can use the {ref}/analysis-porterstem-tokenfilter.html[porter_stem] or {ref}/analysis-kstem-tokenfilter.html[kstem] token filter directly, or create a language-specific Snowball stemmer with the {ref}/analysis-snowball-tokenfilter.html[snowball] token filter, all of the algorithmic stemmers are exposed via a single unified interface: the {ref}/analysis-stemmer-tokenfilter.html[stemmer token filter], which accepts the language parameter.

For instance, perhaps you find the default stemmer used by the english analyzer to be too aggressive and you want to make it less aggressive. The first step is to look up the configuration for the english analyzer in the {ref}/analysis-lang-analyzer.html[language analyzers] documentation, which shows the following:

1. The keyword_marker token filter lists words that should not be stemmed. This defaults to the empty list.

2. The english analyzer uses two stemmers: the possessive_english and the english stemmer. The possessive stemmer removes 's from any words before passing them on to the english_stop, english_keywords, and english_stemmer.

Having reviewed the current configuration, we can use it as the basis for a new analyzer, with the following changes:

• Change the english_stemmer from english (which maps to the {ref}/analysis-porterstem-tokenfilter.html[porter_stem] token filter) to light_english (which maps to the less aggressive {ref}/analysis-kstem-tokenfilter.html[kstem] token filter).

• Add the asciifolding token filter to remove any diacritics from foreign words.

• Remove the keyword_marker token filter, as we don’t need it. (We discuss this in more detail in [controlling-stemming].)

Our new custom analyzer would look like this:

1. Replaced the english stemmer with the less aggressive light_english stemmer

2. Added the asciifolding token filter