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https://issues.apache.org/jira/browse/OPENNLP-777?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14632418#comment-14632418
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Cohan Sujay Carlos edited comment on OPENNLP-777 at 7/18/15 1:24 PM:
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I've attached files demonstrating how the Naive Bayesian document categorizer
(DocumentCategorizerNB) may be trained and used for document classification.
These Java files (D1TopicClassifierTrainingDemo and D1TopicClassifierUsageDemo)
are meant to be used with the training data file (topics.train) that you will
also find in the attachments.
When training said categorizer, place 'topics.train' in a 'corpora/topics'
directory under the directory where you are running this code.
The model will be created in the sub-folder 'models' as 'topics_nb.bin' (make
sure you have a folder by that name under your current directory).
D1TopicClassifierUsageDemo will use that model file to classify some documents.
was (Author: cohan.sujay):
Files demonstrating how the Naive Bayesian document categorizer
(DocumentCategorizerNB) may be trained and used for document classification.
These Java files are meant to be used with the training data file
(topics.train) that you will also find in the attachments.
When training said categorizer, place 'topics.train' in a 'corpora/topics'
directory under the directory where you are running this code. The model will
be created in the sub-folder 'models' (make sure you have a folder by that name
under your current directory).
> Naive Bayesian Classifier
> -------------------------
>
> Key: OPENNLP-777
> URL: https://issues.apache.org/jira/browse/OPENNLP-777
> Project: OpenNLP
> Issue Type: New Feature
> Components: Machine Learning
> Environment: J2SE 1.5 and above
> Reporter: Cohan Sujay Carlos
> Priority: Minor
> Labels: NBClassifier, bayes, bayesian, classifier, multinomial,
> naive
> Attachments: D1TopicClassifierTrainingDemoNB.java,
> D1TopicClassifierUsageDemoNB.java,
> naive-bayesian-classifier-for-opennlp-1.6.0-rc6-with-test-cases.patch,
> topics.train
>
> Original Estimate: 504h
> Remaining Estimate: 504h
>
> I thought it would be nice to have a Naive Bayesian classifier in OpenNLP (it
> lacks one at present).
> Implementation details: We have a production-hardened piece of Java code for
> a multinomial Naive Bayesian classifier (with default Laplace smoothing) that
> we'd like to contribute. The code is Java 1.5 compatible. I'd have to write
> an adapter to make the interface compatible with the ME classifier in
> OpenNLP. I expect the patch to be available 1 to 3 weeks from now.
> Below is the email trail of a discussion in the dev mailing list around this
> dated May 19th, 2015.
> <snip>
> Tommaso Teofili via opennlp.apache.org
> to dev
> Hi Cohan,
> I think that'd be a very valuable contribution, as NB is one of the
> foundation algorithms, often used as basis for comparisons.
> It would be good if you could create a Jira issue and provide more details
> about the implementation and, eventually, a patch.
> Thanks and regards,
> Tommaso
> </snip>
> 2015-05-19 9:57 GMT+02:00 Cohan Sujay Carlos
> > I have a question for the OpenNLP project team.
> >
> > I was wondering if there is a Naive Bayesian classifier implementation in
> > OpenNLP that I've not come across, or if there are plans to implement one.
> >
> > If it is the latter, I should love to contribute an implementation.
> >
> > There is an ME classifier already available in OpenNLP, of course, but I
> > felt that there was an unmet need for a Naive Bayesian (NB) classifier
> > implementation to be offered as well.
> >
> > An NB classifier could be bootstrapped up with partially labelled training
> > data as explained in the Nigam, McCallum, et al paper of 2000 "Text
> > Classification from Labeled and Unlabeled Documents using EM".
> >
> > So, if there isn't an NB code base out there already, I'd be happy to
> > contribute a very solid implementation that we've used in production for a
> > good 5 years.
> >
> > I'd have to adapt it to load the same training data format as the ME
> > classifier, but I guess that shouldn't be very difficult to do.
> >
> > I was wondering if there was some interest in adding an NB implementation
> > and I'd love to know who could I coordinate with if there is?
> >
> > Cohan Sujay Carlos
> > CEO, Aiaioo Labs, India
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