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https://issues.apache.org/jira/browse/LUCENE-8633?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16741936#comment-16741936
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Alan Woodward commented on LUCENE-8633:
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I added a configurable pivot and exponent - by default we go for a saturation
function with pivot of 1, but you can tune the pivot, and adding an exponent
makes it use a sigmoid function instead (sigmoid with an exponent of 1 is
equivalent to the saturation function). I looked at adding the log function as
well, but a) it isn't bounded in the same way, and b) it ends up exposing the
IntervalScoreFunction as a public interface, which I'd rather not do.
> Remove term weighting from interval scoring
> -------------------------------------------
>
> Key: LUCENE-8633
> URL: https://issues.apache.org/jira/browse/LUCENE-8633
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Alan Woodward
> Assignee: Alan Woodward
> Priority: Major
> Attachments: LUCENE-8633.patch, LUCENE-8633.patch
>
>
> IntervalScorer currently uses the same scoring mechanism as SpanScorer,
> summing the IDF of all possibly matching terms from its parent
> IntervalsSource and using that in conjunction with a sloppy frequency to
> produce a similarity-based score. This doesn't really make sense, however,
> as it means that terms that don't appear in a document can still contribute
> to the score, and appears to make scores from interval queries comparable
> with scores from term or phrase queries when they really aren't.
> I'd like to explore a different scoring mechanism for intervals, based purely
> on sloppy frequency and ignoring term weighting. This should make the scores
> easier to reason about, as well as making them useful for things like
> proximity boosting on boolean queries.
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