How to create a process to already prepared data (NLP, NN)

Maja

Hi!
I faced a huge problem in Rapidminer. So basically I have a dataset of word vectors which are categorized to 5 labels. I'd like to train a neural network how to associate proper combinations (in this case - appearance of words in sentences) to one of the 5 labels. The problem is that I do not know how to design an useful process. I've watched a lot of tutorials for Rapidminer, but unfortunately there's not enough similarities to my project. 

Could you please help me? This is my engineer thesis and I'd really love to finish it properly ))

Thanks in advance!

PS  - 9.csv file is an output, arxiv(...).txt file is an example input. 

kayman

If I get it right wordlist to data is what you need, I used your input to create similar output and this is what I got : 



This is the simplified process : 

<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.3.001" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="UTF-8"/>
    <process expanded="true">
      <operator activated="true" class="read_csv" compatibility="9.3.001" expanded="true" height="68" name="Read CSV" width="90" x="112" y="34">
        <parameter key="csv_file" value="C:\Users\yourfolder\Downloads\arxiv_annotate3_80_1.txt"/>
        <parameter key="column_separators" value="&#9;"/>
        <parameter key="trim_lines" value="false"/>
        <parameter key="use_quotes" value="true"/>
        <parameter key="quotes_character" value="&quot;"/>
        <parameter key="escape_character" value="\"/>
        <parameter key="skip_comments" value="true"/>
        <parameter key="comment_characters" value="#"/>
        <parameter key="starting_row" value="1"/>
        <parameter key="parse_numbers" value="true"/>
        <parameter key="decimal_character" value="."/>
        <parameter key="grouped_digits" value="false"/>
        <parameter key="grouping_character" value=","/>
        <parameter key="infinity_representation" value=""/>
        <parameter key="date_format" value=""/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations"/>
        <parameter key="time_zone" value="SYSTEM"/>
        <parameter key="locale" value="English (United States)"/>
        <parameter key="encoding" value="UTF-8"/>
        <parameter key="read_all_values_as_polynominal" value="false"/>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="label.true.polynominal.label"/>
          <parameter key="1" value="txt.true.text.attribute"/>
        </list>
        <parameter key="read_not_matching_values_as_missings" value="false"/>
        <parameter key="datamanagement" value="double_array"/>
        <parameter key="data_management" value="auto"/>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="8.2.000" expanded="true" height="82" name="Process Documents from Data" width="90" x="246" y="34">
        <parameter key="create_word_vector" value="true"/>
        <parameter key="vector_creation" value="Binary Term Occurrences"/>
        <parameter key="add_meta_information" value="true"/>
        <parameter key="keep_text" value="false"/>
        <parameter key="prune_method" value="none"/>
        <parameter key="prune_below_percent" value="3.0"/>
        <parameter key="prune_above_percent" value="30.0"/>
        <parameter key="prune_below_rank" value="0.05"/>
        <parameter key="prune_above_rank" value="0.95"/>
        <parameter key="datamanagement" value="double_sparse_array"/>
        <parameter key="data_management" value="auto"/>
        <parameter key="select_attributes_and_weights" value="false"/>
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" compatibility="8.2.000" expanded="true" height="68" name="Tokenize" width="90" x="246" y="34">
            <parameter key="mode" value="regular expression"/>
            <parameter key="characters" value=".:"/>
            <parameter key="expression" value="\s"/>
            <parameter key="language" value="English"/>
            <parameter key="max_token_length" value="3"/>
          </operator>
          <connect from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" 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="text:wordlist_to_data" compatibility="8.2.000" expanded="true" height="82" name="WordList to Data" width="90" x="380" y="85"/>
      <connect from_op="Read CSV" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_port="result 1"/>
      <connect from_op="Process Documents from Data" from_port="word list" to_op="WordList to Data" to_port="word list"/>
      <connect from_op="WordList to Data" from_port="example set" to_port="result 2"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
      <portSpacing port="sink_result 3" spacing="0"/>
    </process>
  </operator>
</process>