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  Published Paper Details:

  Paper Title

KNOWLEDGE EXTRACTION FOR AUTOMATED RECRUITMENT PROCESS USING NATURAL LANGUAGE PROCESSING

  Authors

  Sushit Kumar,  Shalini Bhadola,  Kirti Bhatia

  Keywords

Natural Language Processing (NLP), Knowledge extraction

  Abstract


When companies post jobs to their sites or other sites then hundreds of applications are received. Reading these CVs and recruiting the right candidates is a work that will include higher workforce to getting the result manually. Parsing Resume and scanning the same is an old process and estimating his proficiency in certain areas are practiced earlier. If we see currently there are no open-source CV parsers are available to extract knowledge from CV with expected results and therefore the purpose of the dissertation is to remove the shortcoming in the current Recruitment Systems which are used for extracting information from available CV thatwas available with the organization. With this research dissertation we are trying to generate solution to problem and provides accuracy in results and does the work faster.CV/Resumestakenfromdifferentprofessions use different formats, fonts, styling and structuring and vocabulary. We are focusing the concept of Automated Recruitment process and the mail logic behind it is to provide ease to Recruitment Process with the help of Technology.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2007564

  Paper ID - 197410

  Page Number(s) - 5129-5137

  Pubished in - Volume 8 | Issue 7 | July 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Sushit Kumar,  Shalini Bhadola,  Kirti Bhatia,   "KNOWLEDGE EXTRACTION FOR AUTOMATED RECRUITMENT PROCESS USING NATURAL LANGUAGE PROCESSING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 7, pp.5129-5137, July 2020, Available at :http://www.ijcrt.org/papers/IJCRT2007564.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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