TestExtractor Tutorial

 

Extraction means extracting types within documents (such as names or places.) TrainTestExtractor tasks use text data.  For this example we will use sample1.test as the testing data.  These samples are built into the code, so they require no additional setup. To see how to label and load your own data for this task, look at the Labeling and Loading Data Tutorial.

 

This experiment loads an extractor which has been saved by running TrainExtractor and tests the extractors performance on labeled test data.  The experiment outputs statistics on token and span precision, recall, and error rates.

 

Using the gui:

1)      To run this type of task using the gui type:

java –Xmx500M edu.cmu.minorthird.ui.TestExtractor –gui

A window will appear.  The view and change the parameters of the experiment press the Edit Button located next to TrainTestExtractor.  A PropertyEditor will appear:

2)      To view what each parameter does and/or how to set it, click the “?” button next to each field.  The parameters that must be entered for the experiment to run are additionalParameters (-loadFrom), baseParameters (-labels) and signalParameters (-spanType or –spanProp)  All other parameters have default or are not needed for the result.  There are 4 bunches of parameters that can be modified for running a TrainTestExtractor experiment:

1.      Name the saved extractor in the loadFrom text field.  (If you do not have a saved extractor, look at the TrainExtractor Tutorial to find how to create one)

2.      Specify the testing data for the experiment must be entered by specifying a labelsFilename.  Since the samples are built into the code, sample1.test can simply be typed into the TextField under labelsFilename to load the data.  Note: data from a directory can be loaded by using the browse button.

3.      To save the results from the experiment, enter a file to which to write the results in the sasveAs text field.  Note: this is optional, but useful for comparing results later

4.      Once labelsFilename is specified, click the “Edit” button next to signalParamters.  IMPORTANT:  labelsFilename must be specified BEFORE clicking “Edit”.  Another Property Editor will appear. 

                        Select the trueName from the pull down menu.  Then press the “OK” button to close the PropertyEditor for signalParameters

3)      Feel free to try changing any of the other parameters including the ones in advanced options.  Click on the help buttons to get a feeling for what each parameter does and how changing it may affect your results.  Once all the parameters are set, click the “OK” button on the PropertyEditor.

4)      Press the Show Labels button if you would like to view the input data for the extraction task.  This will pop up the same TextBaseViewer that you would see if you ran ViewLabels on the train data.

5)      Now press Start Task under execution controls.  The task will vary in the amount of time it takes depending on the size of the data set, but extraction tasks usually take a minute or two.  When the task is finished, the error rates will appear in the Error messages and output text area along with the total time it takes to run the experiment.

6)      Now that the experiment has run, the results can be seen.  In order to look at the details of your results, click the View Results Button in the Execution Controls section.  Click on the Evaluation tab to see the precision rates of the experiment.  Unless showTestDetails has been deselected (in the advancedOptions menu of splitter parameters), there will be a Full Test Set tab.  When this tab is selected, one can compare whatever is labeled (in this case name) to what the learner predicted.  When comparing green mean true positive, blue means false negative, and yellow mean false positive.  You can also click on the spanTypes tab and select a color and a spam type to highlight.  Make sure that you reset controls before highlighting or comparing.  After making a selection, click Apply to see the result.

To view the precision rates of the experiment, click the Evaluation tab at the top of the window.

Precision = # units predicted correctly/ # units predicted

Recall = # of units predicted correctly/ # total units

F1 = overall evaluation of performance

For all these statistics, higher numbers are better

7)      Press the “Clear Window” button to clear all output from the output and error messages window.  This is useful if you would like to run another experiment.

 

Using the Command Line:

1)        To get started using the command line for a classification experiment type:

java –Xmx500M edu.cmu.minorthird.ui.TrainTestExtractor –help

This will list all the command line arguments that you can use.

Note: You can enter as many command line arguments as you like along with the –gui argument.  This way you can use the command line to specify the parameters that you would like and use the gui to set any additional parameters or view the results

2)                    Show options: specifying these options allow one to pop up informative windows from the command line

a.       -showData –interactively show the dataset in a new window

b.      –showLabels – view the training data and its labels

c.       –showResult – displays the experiment result in a new window (see step 6 in Using the gui)

3)        The first thing you probably want to enter on the command line is the data you would like to train or train/test on.  To do this type –labels and the repository key of the dataset you would like to use.  For this experiment you should type:            –labels sample1.train

4)        Now you either want to specify the saved extractor you would like to load by typing –loadFrom sample1.ann

5)        The next necessary parameter to name is either spanProp or spanType.  To specify this parameter, type –spanType TYPE.  For this dataset TYPE can either be real or spam, so type: -spanType trueName.

6)        Specifiying complex parameters on the command line using the –other option:

To specify anything on the command line you can use the -other option.  The way to do this is specify the path name and what it equals.  For example: to set history size:
-other learner.historySize=3
or to change the numberOfEpochs in the SequenceClassifierLearner
-other learner.sequenceClassifierLearner.numberOfEpochs=10
Basically all one has to do is look in the gui and see all the property names, every time you press the edit button you need to add the property you are editing to the path.  So if you want to edit something in learner you can specify -other learner.PROPERTY_NAME.  Also here is how to specify a class with the other option:
-other learner.spanFeatureExtractor="ui.Recommended.DocumentFE"
To find the correct class names look at the gui or javadocs to see what options are available and there full class name.