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https://issues.apache.org/jira/browse/LUCENE-4345?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13445867#comment-13445867
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Tommaso Teofili commented on LUCENE-4345:
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bq. docsWithClassSize should ideally be terms.getDocCount() for the field as
well rather than maxDoc.
yep, the early assumption here was that all the docs have a value for the class
field but your suggestion is good.
bq. docCount() should not do a search, instead I think it should just return
IR.docFreq(term) ?
correct
bq. it seems we dont need this classCount map at all, nor the priors map?
yes and no, having the priors map slows the training phase (each time it needs
to recompute the priors for all the classes), but fasten the classification
task with the unseen text (it's a cache in the end), wrt the classCount I agree
with you it could be easily replaced (with TermsEnum).
bq. Instead we would just tokenize each doc a single time, and compute the
prior of the terms
we find on the fly (it seems to just be
you mean because of the likelihood calculation tokenizing the same doc multiple
times (|terms in the class field|), right? That'd be surely good, I'll work on
improving that.
Thanks Robert :)
> Create a Classification module
> ------------------------------
>
> Key: LUCENE-4345
> URL: https://issues.apache.org/jira/browse/LUCENE-4345
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Tommaso Teofili
> Assignee: Tommaso Teofili
> Priority: Minor
> Attachments: LUCENE-4345.patch, SOLR-3700_2.patch, SOLR-3700.patch
>
>
> Lucene/Solr can host huge sets of documents containing lots of information in
> fields so that these can be used as training examples (w/ features) in order
> to very quickly create classifiers algorithms to use on new documents and /
> or to provide an additional service.
> So the idea is to create a contrib module (called 'classification') to host a
> ClassificationComponent that will use already seen data (the indexed
> documents / fields) to classify new documents / text fragments.
> The first version will contain a (simplistic) Lucene based Naive Bayes
> classifier but more implementations should be added in the future.
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