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Posted to issues@opennlp.apache.org by "Jason Baldridge (JIRA)" <ji...@apache.org> on 2011/06/08 06:43:58 UTC
[jira] [Resolved] (OPENNLP-155) unreliable training set accuracy in
perceptron
[ https://issues.apache.org/jira/browse/OPENNLP-155?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jason Baldridge resolved OPENNLP-155.
-------------------------------------
Resolution: Fixed
Oops, should have used this to post my notes. Anyway, issue is resolved. Here are the notes again. -Jason
- Changed the update to be the actual perceptron update: when a label
that is not the gold label is chosen for an event, the parameters
associated with that label are decremented, and the parameters
associated with the gold label are incremented. I checked this
empirically on several datasets, and it works better than the
previous update (and it involves fewer updates).
- stepsize is decreased by stepsize/1.05 on every iteration, ensuring
better stability toward the end of training. This is actually the
main reason that the training set accuracy obtained during parameter
update continued to be different from that computed when parameters
aren't updated. Now, the parameters don't jump as much in later
iterations, so things settle down and those two accuracies converge
if enough iterations are allowed.
- Training set accuracy is computed once per iteration.
- Training stops if the current training set accuracy changes less
than a given tolerance from the accuracies obtained in each of the
previous three iterations.
- Averaging is done differently than before. Rather than doing an
immediate update, parameters are simply accumulated after iterations
(this makes the code much easier to understand/maintain). Also, not
every iteration is used, as this tends to give to much weight to the
final iterations, which don't actually differ that much from one
another. I tried a few things and found a simple method that works
well: sum the parameters from the first 20 iterations and then sum
parameters from any further iterations that are perfect squares (25,
36, 49, etc). This gets a good (diverse) sample of parameters for
averaging since the distance between subsequent parameter sets gets
larger as the number of iterations gets bigger.
- Added prepositional phrase attachment dataset to
src/test/resources/data/ppa. This is done with permission from
Adwait Ratnarparkhi -- see the README for details.
- Created unit test to check perceptron training consistency, using
the prepositional phrase attachment data. It would be good to do the
same for maxent.
- Added ListEventStream to make a stream out of List<Event>
- Added some helper methods, e.g. maxIndex, to simplify the code in
the main algorithm.
- The training stats aren't shown for every iteration. Now it is just
the first 10 and then every 10th iteration after that.
- modelDistribution, params, evalParams and others are no longer class
variables. They have been pushed into the findParameters
method. Other variables could/should be made non-global too, but
leaving as is for now.
> unreliable training set accuracy in perceptron
> ----------------------------------------------
>
> Key: OPENNLP-155
> URL: https://issues.apache.org/jira/browse/OPENNLP-155
> Project: OpenNLP
> Issue Type: Improvement
> Components: Maxent
> Affects Versions: maxent-3.0.1-incubating
> Reporter: Jason Baldridge
> Assignee: Jason Baldridge
> Priority: Minor
> Fix For: maxent-3.0.1-incubating, tools-1.5.1-incubating
>
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> The training accuracies reported during perceptron training were much higher than final training accuracy, which turned out to be an artifact of the way training examples were ordered.
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