Resume Classification: A Case Study


  • Professional recruiters and HR personnel manually review hundreds of resumes.
  • Manual sorting of resumes into categories is inaccurate and time-consuming.
  • No resume summaries are available.

Xpertnest’s Solutions

  • A Named Entity Recognizer (NER) was trained to detect key words and key phrases from the resume.
  • Data was annotated for training.
  • The NER used the presence or absence of desired keywords for sorting.
  • Resume summaries were created based on key words and phrases found in the resumes.

Value Delivered

  • NER reduced the risk of human error.
  • Summaries enabled HR personnel to review resumes quickly and decide whether candidates were suitable.
  • The client saved time and was able to raise the quality of its candidates.

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