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Is it possible to Extract definitions of the same concept on different papers?

AndreriwAndreriw Member Posts: 4 Contributor I
edited August 2 in Help
I was thinking:

1. on word like documents 
2. search key word "X"
3. extract the text with the  main words associated to the key word 
4. Is there a template here?

thank you

Answers

  • rfuentealbarfuentealba Moderator, RapidMiner Certified Analyst, Member, University Professor Posts: 563   Unicorn
    It is possible. There is an extension named "Text Mining" that allows you to read Word documents.

    Also in this thread, I created a building block for such a task:

    Import a Word document to Rapidminer — RapidMiner Community

    The rest is to play along with transform case, tokenize and a little more search, but it's doable. And while I think there is a template for basic NLP, it's not complete, I think.
  • MarcoBarradasMarcoBarradas Administrator, Employee, RapidMiner Certified Analyst, Member Posts: 229   Unicorn
    Hi @Andreriw,

    Adding to what Rodrigo shared you can use this basic process to continue your analysis.

    You can also check the new CoreNLP extension. To extract some entities you can also extend it with your own entities.

    https://youtu.be/XvGUFOU1vcI


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            <parameter key="date_format" value="yyyy-MM-dd HH:mm:ss"/>
            <parameter key="time_zone" value="SYSTEM"/>
            <parameter key="input_csv_text" value="word&#10;Birds&#10;Jaguar&#10;Monarch Butterflies"/>
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                <parameter key="macro" value="word"/>
                <parameter key="macro_type" value="data_value"/>
                <parameter key="statistics" value="average"/>
                <parameter key="attribute_name" value="word"/>
                <parameter key="example_index" value="%{example}"/>
                <list key="additional_macros"/>
              </operator>
              <operator activated="true" class="recall" compatibility="9.10.000" expanded="true" height="68" name="Recall" width="90" x="179" y="85">
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                <parameter key="remove_from_store" value="false"/>
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              <operator activated="true" class="text:process_document_from_data" compatibility="9.3.001" expanded="true" height="82" name="Process Documents from Data" width="90" x="380" y="34">
                <parameter key="create_word_vector" value="false"/>
                <parameter key="vector_creation" value="TF-IDF"/>
                <parameter key="add_meta_information" value="true"/>
                <parameter key="keep_text" value="true"/>
                <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="9.3.001" expanded="true" height="68" name="Tokenize" width="90" x="246" y="34">
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                    <parameter key="string" value="%{word}"/>
                    <parameter key="case_sensitive" value="false"/>
                    <parameter key="invert condition" value="false"/>
                    <description align="center" color="yellow" colored="true" width="126">Here you use the key word you are using as a filter</description>
                  </operator>
                  <operator activated="true" class="text:extract_length" compatibility="9.3.001" expanded="true" height="68" name="Extract Length" width="90" x="514" y="34">
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                  <connect from_op="Tokenize" from_port="document" to_op="Filter Tokens (by Content)" to_port="document"/>
                  <connect from_op="Filter Tokens (by Content)" from_port="document" to_op="Extract Length" to_port="document"/>
                  <connect from_op="Extract Length" from_port="document" to_port="document 1"/>
                  <portSpacing port="source_document" spacing="0"/>
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                  <portSpacing port="sink_document 2" spacing="0"/>
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                <description align="center" color="yellow" colored="true" width="126">We keep only the rows that have sentences with our keyword</description>
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              <connect from_port="example set" to_op="Extract Macro" to_port="example set"/>
              <connect from_op="Recall" from_port="result" to_op="Process Documents from Data" to_port="example set"/>
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              <portSpacing port="sink_output 2" spacing="0"/>
            </process>
            <description align="center" color="yellow" colored="true" width="126">Loop through the keywords list</description>
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          <connect from_op="Texts" from_port="output" to_op="Nominal to Text" to_port="example set input"/>
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          <portSpacing port="sink_result 1" spacing="0"/>
          <portSpacing port="sink_result 2" spacing="0"/>
        </process>
      </operator>
    </process>


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