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https://issues.apache.org/jira/browse/OPENNLP-59?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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William Colen resolved OPENNLP-59.
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Resolution: Fixed
> Bad precision using FMeasure
> ----------------------------
>
> Key: OPENNLP-59
> URL: https://issues.apache.org/jira/browse/OPENNLP-59
> Project: OpenNLP
> Issue Type: Bug
> Affects Versions: tools-1.5.1-incubating
> Reporter: William Colen
> Assignee: William Colen
> Fix For: tools-1.5.1-incubating
>
>
> I noticed bad precision in FMeasure results. I think the issue is that the
> current implementation is summing divisions. It computes the precision and
> recall for every sample, and after adds the results for each sample to
> compute the overall result. By doing that, the error related to each division
> are summed and can impact the final result.
> I found the problem while implementing the ChunkerEvaluator. To verify the
> evaluator I tried to compare the results we get using OpenNLP and the Perl
> script conlleval available at
> http://www.cnts.ua.ac.be/conll2000/chunking/output.html. The results were
> always different if I process more than one sentence, because the
> implementation was using FMeasure.updateScores() that was summing divisions.
> To solve that and have the same results provided by conll I basically stopped
> using the Mean class.
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