"Text Classification with different terms"

I would like to classify an example set based on a classification model generated from a related but different example set. The terms will not be identical. Is it reasonable to supply the word list form the model to the example set I wish to classify?

The model I am experimenting with is listed below. It seems to give pretty decent results but I have yet to give it full check (this would require a lot of data preparation).

Any feedback appreciated!

<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.0">
  <context>
    <input>
      <location/>
    </input>
    <output>
      <location/>
      <location/>
    </output>
    <macros/>
  </context>
  <operator activated="true" class="process" expanded="true" name="Process">
    <process expanded="true" height="448" width="748">
      <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve" width="90" x="45" y="75">
        <parameter key="repository_entry" value="team_x_risks_no_dups"/>
      </operator>
      <operator activated="true" class="nominal_to_text" expanded="true" height="76" name="Nominal to Text" width="90" x="179" y="75">
        <parameter key="attribute_filter_type" value="subset"/>
        <parameter key="attribute" value="risk_title risk_desc_risk_keywords_risk_factor_description"/>
        <parameter key="attributes" value="risk_all"/>
      </operator>
      <operator activated="true" class="retrieve" expanded="true" height="60" name="Retrieve (2)" width="90" x="45" y="300">
        <parameter key="repository_entry" value="team_x_risk_cats"/>
      </operator>
      <operator activated="true" class="nominal_to_text" expanded="true" height="76" name="Nominal to Text (2)" width="90" x="179" y="300"/>
      <operator activated="true" class="text:process_document_from_data" expanded="true" height="76" name="Process Documents from Data" width="90" x="313" y="300">
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" expanded="true" height="60" name="Tokenize" width="90" x="45" y="75"/>
          <operator activated="true" class="text:transform_cases" expanded="true" height="60" name="Transform Cases (2)" width="90" x="179" y="210"/>
          <operator activated="true" class="text:filter_stopwords_english" expanded="true" height="60" name="Filter Stopwords (2)" width="90" x="313" y="300"/>
          <operator activated="true" class="text:filter_by_length" expanded="true" height="60" name="Filter Tokens (2)" width="90" x="458" y="288">
            <parameter key="min_chars" value="3"/>
          </operator>
          <operator activated="true" class="text:generate_n_grams_terms" expanded="true" height="60" name="Generate n-Grams (2)" width="90" x="715" y="120"/>
          <connect from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_op="Transform Cases (2)" to_port="document"/>
          <connect from_op="Transform Cases (2)" from_port="document" to_op="Filter Stopwords (2)" to_port="document"/>
          <connect from_op="Filter Stopwords (2)" from_port="document" to_op="Filter Tokens (2)" to_port="document"/>
          <connect from_op="Filter Tokens (2)" from_port="document" to_op="Generate n-Grams (2)" to_port="document"/>
          <connect from_op="Generate n-Grams (2)" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="k_nn" expanded="true" height="76" name="k-NN" width="90" x="447" y="300">
        <parameter key="measure_types" value="NumericalMeasures"/>
        <parameter key="numerical_measure" value="CosineSimilarity"/>
      </operator>
      <operator activated="true" class="text:process_document_from_data" expanded="true" height="76" name="Process Documents from Data (2)" width="90" x="313" y="75">
        <parameter key="keep_text" value="true"/>
        <parameter key="prune_method" value="percentual"/>
        <parameter key="prune_above_percent" value="50.0"/>
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" expanded="true" height="60" name="Tokenize (2)" width="90" x="45" y="30"/>
          <operator activated="true" class="text:transform_cases" expanded="true" height="60" name="Transform Cases" width="90" x="179" y="75"/>
          <operator activated="true" class="text:filter_stopwords_english" expanded="true" height="60" name="Filter Stopwords (English)" width="90" x="313" y="210"/>
          <operator activated="true" class="text:filter_by_length" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="447" y="120">
            <parameter key="min_chars" value="3"/>
          </operator>
          <operator activated="true" class="text:generate_n_grams_terms" expanded="true" height="60" name="Generate n-Grams (Terms)" width="90" x="514" y="30"/>
          <connect from_port="document" to_op="Tokenize (2)" to_port="document"/>
          <connect from_op="Tokenize (2)" from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
          <connect from_op="Filter Stopwords (English)" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
          <connect from_op="Filter Tokens (by Length)" from_port="document" to_op="Generate n-Grams (Terms)" to_port="document"/>
          <connect from_op="Generate n-Grams (Terms)" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="apply_model" expanded="true" height="76" name="Apply Model" width="90" x="581" y="165">
        <list key="application_parameters"/>
      </operator>
      <connect from_op="Retrieve" from_port="output" to_op="Nominal to Text" to_port="example set input"/>
      <connect from_op="Nominal to Text" from_port="example set output" to_op="Process Documents from Data (2)" to_port="example set"/>
      <connect from_op="Retrieve (2)" from_port="output" to_op="Nominal to Text (2)" to_port="example set input"/>
      <connect from_op="Nominal to Text (2)" from_port="example set output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_op="k-NN" to_port="training set"/>
      <connect from_op="Process Documents from Data" from_port="word list" to_op="Process Documents from Data (2)" to_port="word list"/>
      <connect from_op="k-NN" from_port="model" to_op="Apply Model" to_port="model"/>
      <connect from_op="Process Documents from Data (2)" from_port="example set" to_op="Apply Model" to_port="unlabelled data"/>
      <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>