GATE car problems

I am using gate.ac.uk GUI for text data of mines and now I am trying to use its machine learning module. To do this, I created some XML schemas to load into GATE. Here's one example:

<?xml version="1.0"?>
<schema xmlns="http://www.w3.org/2000/10/XMLSchema">
  <!-- XSchema definition for Condition -->
  <element name="Condition">
    <complexType>
      <attribute name="attrb_ConditionStatus" use="optional" value="other">
        <simpleType>
          <restriction base="string">
          <enumeration value="value_condition"/>
            </restriction>
          </simpleType>
      </attribute>
    </complexType>
  </element>
</schema>

      

        
        
        
      

    

I created a similar schema for every attribute that I want to annotate. I will illustrate the step that I am implementing after creating the schemas: 1. I load the Schematic Annotation Editor for this purpose, and then load the customized schemas through the Language Resources menu item. 2. I also upload documents and corpus. 3. Then I run Annie 4. I see the custom schema in the Annotations tab of the document 5. I comment on terms with my custom annotations

Now I want to run machine learning through the "Learning-Batch Learning PR" plugin. I have added a processing resource to my application line. My problem is about generating a machine learning configuration file / schema, I searched the web but couldn't figure out how to create the schema correctly. I've looked at various examples, here's my attempt:

<?xml version="1.0"?>
<ML-CONFIG>
  <VERBOSITY level="1"/>
  <SURROUND value="true"/>
  <PARAMETER name="thresholdProbabilityEntity" value="0.2"/>
  <PARAMETER name="thresholdProbabilityBoundary" value="0.4"/>
  <multiClassification2Binary method="one-vs-others"/>
  <EVALUATION method="holdout" ratio="0.66"/>
  <ENGINE nickname="PAUM" implementationName="PAUM"
        options="-p 50 -n 5 -optB 0.3"/>
  <DATASET>
    <INSTANCE-TYPE>Token</INSTANCE-TYPE>
    <ATTRIBUTELIST>
       <NAME>ManType</NAME>
       <SEMTYPE>NOMINAL</SEMTYPE>
       <TYPE>Manufactuer</TYPE>
       <FEATURE>category</FEATURE>
       <RANGE from="-2" to="2"/>
    </ATTRIBUTELIST>
    <ATTRIBUTELIST>
       <NAME>ModelType</NAME>
       <SEMTYPE>NOMINAL</SEMTYPE>
       <TYPE>Model</TYPE>
       <FEATURE>orth</FEATURE>
       <RANGE from="-2" to="2"/>
    </ATTRIBUTELIST>
     <ATTRIBUTE>
       <NAME>Class1</NAME>
       <SEMTYPE>NOMINAL</SEMTYPE>
       <TYPE>Manufacturer</TYPE>
       <FEATURE>majorType</FEATURE>
       <POSITION>0</POSITION>
     </ATTRIBUTE>
     <ATTRIBUTE>
       <NAME>Class2</NAME>
       <SEMTYPE>NOMINAL</SEMTYPE>
       <TYPE>Model</TYPE>
       <FEATURE>type</FEATURE>
       <POSITION>0</POSITION>
       <CLASS/>
     </ATTRIBUTE>
   </DATASET>
</ML-CONFIG>

      

        
        
        
      

    

I want a machine learning algorithm to participate in annotating the manufacturer and the model (types), which is also a custom annotation that I created with a schema. My first question is, does the ml config structure look correct? I add a new Pipelin Corpus, add a Patch Learning PR process, select the Evaluation mode, and then run the application in my tutorial document. This is the result:

The number of threads used is 1
** Evaluation mode started:
Hold-out test: runs=1, ratio of training docs is 0.66
Split, k=1, trainingNum=0.
HOLDOUT Fold 0:   (correct, partialCorrect, spurious, missing)= (0.0, 0.0, 0.0, 0.0);  (precision, recall, F1)= (0.0, 0.0, 0.0);  Lenient: (0.0, 0.0, 0.0)

  *** Averaged results for each label over 1 runs as:

Results of single label:

Overall results as:
  (correct, partialCorrect, spurious, missing)= (0.0, 0.0, 0.0, 0.0);  (precision, recall, F1)= (0.0, 0.0, 0.0);  Lenient: (0.0, 0.0, 0.0)

This learning session finished!

      

        
        
        
      

    

The result shows that something is not configured correctly - either the ml config file or the pipeline I created for this purpose. If anyone can share some thoughts on this, I would be grateful. Again, I've scoured the internet high and low and read a few machine learning tutorials and ppt at gate.ac.uk, but still seems pretty confusing to me.

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