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Combiation of stemming and stop word removal consequences on standard errors


Regular Expressions in WordIncrease F1 score on a text corpusCan I create a word cloud of crowdfunding donors using word cloud?Can we apply community detection algorithms for word vector space?Word embedding vectors for keyphrase extractionBuild a relevancy scoring model of articles using NLPPlots with shaded standard deviationword/sentence alignment for English documentWord embeddings for Information Retrieval - Document search?Product classification in hierarchical categories based on multiple parameters and non-standard descriptions













0












$begingroup$


I've read an article of Greene, Ceron, Schumacher and Fazekas which called The Nuts and Bolts of Automated Text Analysis: Comparing Different Document Pre-Processing Techniques in FourCountries.



In this article, the authors state that using stemming and stop word removing separately decreases the standard errors of scaling estimations (compared to no-preprocessed texts). But when using the combination of them, it increases it.



So can you help me why using these techniques separately it increases the precision while using the combination it the S.E. go higher.



Thanks in advance, J










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    0












    $begingroup$


    I've read an article of Greene, Ceron, Schumacher and Fazekas which called The Nuts and Bolts of Automated Text Analysis: Comparing Different Document Pre-Processing Techniques in FourCountries.



    In this article, the authors state that using stemming and stop word removing separately decreases the standard errors of scaling estimations (compared to no-preprocessed texts). But when using the combination of them, it increases it.



    So can you help me why using these techniques separately it increases the precision while using the combination it the S.E. go higher.



    Thanks in advance, J










    share|improve this question







    New contributor




    Judge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      I've read an article of Greene, Ceron, Schumacher and Fazekas which called The Nuts and Bolts of Automated Text Analysis: Comparing Different Document Pre-Processing Techniques in FourCountries.



      In this article, the authors state that using stemming and stop word removing separately decreases the standard errors of scaling estimations (compared to no-preprocessed texts). But when using the combination of them, it increases it.



      So can you help me why using these techniques separately it increases the precision while using the combination it the S.E. go higher.



      Thanks in advance, J










      share|improve this question







      New contributor




      Judge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I've read an article of Greene, Ceron, Schumacher and Fazekas which called The Nuts and Bolts of Automated Text Analysis: Comparing Different Document Pre-Processing Techniques in FourCountries.



      In this article, the authors state that using stemming and stop word removing separately decreases the standard errors of scaling estimations (compared to no-preprocessed texts). But when using the combination of them, it increases it.



      So can you help me why using these techniques separately it increases the precision while using the combination it the S.E. go higher.



      Thanks in advance, J







      text-mining






      share|improve this question







      New contributor




      Judge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











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      Judge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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