Like tokenizers, filters consume input and produce a stream of tokens. Filters also derive from
org.apache.lucene.analysis.TokenStream. Unlike tokenizers, a filter’s input is another TokenStream. The job of a filter is usually easier than that of a tokenizer since in most cases a filter looks at each token in the stream sequentially and decides whether to pass it along, replace it or discard it.
A filter may also do more complex analysis by looking ahead to consider multiple tokens at once, although this is less common. One hypothetical use for such a filter might be to normalize state names that would be tokenized as two words. For example, the single token "california" would be replaced with "CA", while the token pair "rhode" followed by "island" would become the single token "RI".
Because filters consume one
TokenStream and produce a new
TokenStream, they can be chained one after another indefinitely. Each filter in the chain in turn processes the tokens produced by its predecessor. The order in which you specify the filters is therefore significant. Typically, the most general filtering is done first, and later filtering stages are more specialized.
<fieldType name="text" class="solr.TextField"> <analyzer> <tokenizer class="solr.StandardTokenizerFactory"/> <filter class="solr.StandardFilterFactory"/> <filter class="solr.LowerCaseFilterFactory"/> <filter class="solr.EnglishPorterFilterFactory"/> </analyzer> </fieldType>
This example starts with Solr’s standard tokenizer, which breaks the field’s text into tokens. Those tokens then pass through Solr’s standard filter, which removes dots from acronyms, and performs a few other common operations. All the tokens are then set to lowercase, which will facilitate case-insensitive matching at query time.
The last filter in the above example is a stemmer filter that uses the Porter stemming algorithm. A stemmer is basically a set of mapping rules that maps the various forms of a word back to the base, or stem, word from which they derive. For example, in English the words "hugs", "hugging" and "hugged" are all forms of the stem word "hug". The stemmer will replace all of these terms with "hug", which is what will be indexed. This means that a query for "hug" will match the term "hugged", but not "huge".
Conversely, applying a stemmer to your query terms will allow queries containing non stem terms, like "hugging", to match documents with different variations of the same stem word, such as "hugged". This works because both the indexer and the query will map to the same stem ("hug").
Word stemming is, obviously, very language specific. Solr includes several language-specific stemmers created by the Snowball generator that are based on the Porter stemming algorithm. The generic Snowball Porter Stemmer Filter can be used to configure any of these language stemmers. Solr also includes a convenience wrapper for the English Snowball stemmer. There are also several purpose-built stemmers for non-English languages. These stemmers are described in Language Analysis.