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https://issues.apache.org/jira/browse/LUCENE-8633?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16740265#comment-16740265
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Alan Woodward commented on LUCENE-8633:
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Attached is a patch with an alternative scoring system:
* Sloppy frequency is calculated as the sum of individual interval scores.
Each interval is scored as 1/(length - minExtent + 1), where minExtent() is a
new method on IntervalsSource that exposes the minimum possible length of an
interval produced by that source. This is based on the scoring mechanism
described in Vigna's paper describing intervals[1]
* In order to keep the score bounded so that it can be used as a proximity
boost without wrecking max-score optimizations, the sloppy frequency is
converted to a score using a saturation function. I've chosen 5 as a pivot
here more-or-less at random (meaning that documents containing 5 intervals of
minimum possible length will get a score of boost * 0.5) - better ways of
choosing a pivot are welcome.
[1]
http://vigna.di.unimi.it/ftp/papers/EfficientAlgorithmsMinimalIntervalSemantics.pdf
> 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
>
>
> 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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