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Posted to dev@lucene.apache.org by "Ahmet Arslan (JIRA)" <ji...@apache.org> on 2015/09/27 18:07:04 UTC
[jira] [Updated] (LUCENE-6818) Implementing Divergence from
Independence (DFI) Term-Weighting for Lucene/Solr
[ https://issues.apache.org/jira/browse/LUCENE-6818?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Ahmet Arslan updated LUCENE-6818:
---------------------------------
Attachment: LUCENE-6818.patch
Patch for DFI. However, with this one {{TestSimilarity2#testCrazySpans}} fails.
Any pointers how to fix this will be really appreciated.
> Implementing Divergence from Independence (DFI) Term-Weighting for Lucene/Solr
> ------------------------------------------------------------------------------
>
> Key: LUCENE-6818
> URL: https://issues.apache.org/jira/browse/LUCENE-6818
> Project: Lucene - Core
> Issue Type: New Feature
> Components: core/query/scoring
> Affects Versions: 5.3
> Reporter: Ahmet Arslan
> Priority: Minor
> Labels: similarity
> Fix For: Trunk
>
> Attachments: LUCENE-6818.patch
>
>
> As explained in the [write-up|http://lucidworks.com/blog/flexible-ranking-in-lucene-4], many state-of-the-art ranking model implementations are added to Apache Lucene.
> This issue aims to include DFI model, which is the non-parametric counterpart of the Divergence from Randomness (DFR) framework.
> DFI is both parameter-free and non-parametric:
> * parameter-free: it does not require any parameter tuning or training.
> * non-parametric: it does not make any assumptions about word frequency distributions on document collections.
> It is highly recommended *not* to remove stopwords (very common terms: the, of, and, to, a, in, for, is, on, that, etc) with this similarity.
> For more information see: [A nonparametric term weighting method for information retrieval based on measuring the divergence from independence|http://dx.doi.org/10.1007/s10791-013-9225-4]
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