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From "Jim Ferenczi (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (LUCENE-8633) Remove term weighting from interval scoring
Date Fri, 11 Jan 2019 15:09:00 GMT

    [ https://issues.apache.org/jira/browse/LUCENE-8633?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16740469#comment-16740469
] 

Jim Ferenczi commented on LUCENE-8633:
--------------------------------------

+1 to remove the terms statistics and to rely solely on the number and extent of the intervals.
Choosing the pivot is really difficult though and cannot be computed statistically like the
feature query does. Maybe we should have a default pivot of 1 and make it configurable in
the constructor ? We could also make all feature functions available ? 

> 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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