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https://issues.apache.org/jira/browse/SOLR-2917?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13157054#comment-13157054
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Stanislaw Osinski commented on SOLR-2917:
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bq. That was the idea behind the suggestion. Highlighter works a litle bit
different so it does not need this: it uses the TermVectors only for finding
the highlighting offsets but marks the highligts in the original text (from a
stored field). It just spares to reanalyze again, which can be expensive if you
e.g. use BASIS or whatever heavy analysis.
Yeah, it's a bit different indeed because clustering would need the original
text of the tokens instead of just the start offset and length. Ultimately, the
choice between storing two different token streams and doing the analysis at
runtime is a trade-off between storage size (doubled?) and slower runtime
performance. Once we get Carrot2 to support pre-tokenized input (not hard
conceptually, but tricky in terms of the API), both solutions would be possible.
> Support for field-specific tokenizers, token- and character filters in search
> results clustering
> ------------------------------------------------------------------------------------------------
>
> Key: SOLR-2917
> URL: https://issues.apache.org/jira/browse/SOLR-2917
> Project: Solr
> Issue Type: Improvement
> Components: contrib - Clustering
> Reporter: Stanislaw Osinski
> Assignee: Stanislaw Osinski
> Fix For: 3.6
>
>
> Currently, Carrot2 search results clustering component creates clusters based
> on the raw text of a field. The reason for this is that Carrot2 aims to
> create meaningful cluster labels by using sequences of words taken directly
> from the documents' text (including stop words: _Development of Lucene and
> Solr_ is more readable than _Development Lucene Solr_). The easiest way of
> providing input for such a process was feeding Carrot2 with raw (stored)
> document content.
> It is, however, possible to take into account +some+ of the field's filters
> during clustering. Because Carrot2 does not currently expose an API for
> feeding pre-tokenized input, the clustering component would need to:
> 1. get raw text of the field,
> 2. run it through the field's char filters, tokenizers and selected token
> filters (omitting e.g. stop words filter and stemmers, Carrot2 needs the
> original words to produce readable cluster labels),
> 3. glue the output back into a string and feed to Carrot2 for clustering.
> In the future, to eliminate step 3, we could modify Carrot2 to accept
> pre-tokenized content.
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