I am using this filter as a stemmer for Turkish language. In many academic research (classification, retrieval) it is used and called as Fixed Prefix Stemmer or Simple Truncation Method or F5 in short.
Among F3 TO F7, F5 stemmer (length=5) is found to work well for Turkish language in Information Retrieval on Turkish Texts. It is the same work where most of stopwords_tr.txt are acquired.
ElasticSearch has truncate filter but it does not respect keyword attribute. And it has a use case similar to TruncateFieldUpdateProcessorFactory
Main advantage of F5 stemming is : it does not effected by the meaning loss caused by ascii folding. It is a diacritics-insensitive stemmer and works well with ascii folding. Effects of diacritics on Turkish information retrieval
Here is the full field type I use for "diacritics-insensitive search" for Turkish
I would like to get community opinions :
1) Any interest in this?
2) keyword attribute should be respected?
3) package name analysis.misc versus analyis.tr
4) name of the class TruncateTokenFilter versus FixedPrefixStemFilter