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From "Simon Willnauer (JIRA)" <>
Subject [jira] [Commented] (LUCENE-4345) Create a Classification module
Date Thu, 13 Sep 2012 08:28:08 GMT


Simon Willnauer commented on LUCENE-4345:

hey tommaso, 

I just briefly skimmed through your latest patch and I have a bunch of comments:

* I agree with robert you should build a small inverted index instead of retokenizing. I'd
use a BytesRefHash with a parallel array as we use during indexing, if you have trouble with
this I am happy to update your patch and give you an example.
* I suggest to move the into the while() part like while((next =
!= null) for consistency (in assignClass)
* Can you use BytesRef for fieldNames to safe the conversion everytime.
* Instead of specifying the document as a String you should rather use IndexableField and
in turn pull the tokenstream from IndexableField#tokenStream(Analyzer)
* I didn't see a reason why you use Double instead of double (primitive) as return values,
I think the boxing is unnecessary
* in assignClass can't you reuse the BytesRef returned from the termsEnum for further processing
instead of converting it to a string?
* in getWordFreqForClass you might want to use TotalHitCountCollector since you are only interested
in the number of hits. That collector will not score or collect any documents at all and is
way less complex that the default TopDocsCollector
* I have trouble to understand why the interface expects an atomic reader here. From my perspective
you should handle per-segment aspect internally and instead just use IndexReader in the interface.
* The interface you defined has some problems with respect to Multi-Threading IMO. The interface
itself suggests that this class is stateful and you have to call methods in a certain order
and at the same you need to make sure that it is not published for read access before training
is done. I think it would be wise to pass in all needed objects as constructor arguments and
make the references final so it can be shared across threads and add an interface that represents
the trained model computed offline? In this case it doesn't really matter but in the future
it might make sense. We can also skip the model interface entirely and remove the training
method until we have some impls that really need to be trained.  

> Create a Classification module
> ------------------------------
>                 Key: LUCENE-4345
>                 URL:
>             Project: Lucene - Core
>          Issue Type: New Feature
>            Reporter: Tommaso Teofili
>            Assignee: Tommaso Teofili
>            Priority: Minor
>         Attachments: LUCENE-4345_2.patch, 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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