Bad precision using FMeasure
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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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